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Guo E, Xu L, Zhang D, Zhang J, Zhang X, Bai X, Chen L, Peng Q, Zhang G, Jin Z, Sun H. Diagnostic performance of MRI in detecting prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL: a systematic review and meta-analysis. Insights Imaging 2024; 15:147. [PMID: 38886256 PMCID: PMC11183000 DOI: 10.1186/s13244-024-01699-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/15/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVE To investigate the diagnostic performance of MRI in detecting clinically significant prostate cancer (csPCa) and prostate cancer (PCa) in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. METHODS A computerized search of PubMed, Embase, Cochrane Library, Medline, and Web of Science was conducted from inception until October 31, 2023. We included articles on the use of MRI to detect csPCa or PCa at 4-10 ng/mL PSA. The primary and secondary outcomes were MRI performance in csPCa and PCa detection, respectively; the estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were pooled in a bivariate random-effects model. RESULTS Among the 19 studies (3879 patients), there were 10 (2205 patients) and 13 studies (2965 patients) that reported MRI for detecting csPCa or PCa, respectively. The pooled sensitivity and specificity for csPCa detection were 0.84 (95% confidence interval [CI], 0.79-0.88) and 0.76 (95%CI, 0.65-0.84), respectively, for PCa detection were 0.82 (95%CI, 0.75-0.87) and 0.74 (95%CI, 0.65-0.82), respectively. The pooled NPV for csPCa detection was 0.91 (0.87-0.93). Biparametric magnetic resonance imaging also showed a significantly higher sensitivity and specificity relative to multiparametric magnetic resonance imaging (both p < 0.01). CONCLUSION Prostate MRI enables the detection of csPCa and PCa with satisfactory performance in the PSA gray zone. The excellent NPV for csPCa detection indicates the possibility of biopsy decision-making in patients in the PSA gray zone, but substantial heterogeneity among the included studies should be taken into account. CLINICAL RELEVANCE STATEMENT Prostate MRI can be considered a reliable and satisfactory tool for detecting csPCa and PCa in patients with PSA in the "gray zone", allowing for reducing unnecessary biopsy and optimizing the overall examination process. KEY POINTS Prostate-specific antigen (PSA) is a common screening tool for prostate cancer but risks overdiagnosis. MRI demonstrated excellent negative predictive value for prostate cancer in the PSA gray zone. MRI can influence decision-making for these patients, and biparametric MRI should be further evaluated.
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Affiliation(s)
- Erjia Guo
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Lili Xu
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Daming Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Jiahui Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Xiaoxiao Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Xin Bai
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Li Chen
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Qianyu Peng
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Gumuyang Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
| | - Zhengyu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
- National Center for Quality Control of Radiology, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
| | - Hao Sun
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
- National Center for Quality Control of Radiology, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
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Huang J, He C, Xu P, Song B, Zhao H, Yin B, He M, Lu X, Wu J, Wang H. Development and validation of a clinical-radiomics model for prediction of prostate cancer: a multicenter study. World J Urol 2024; 42:275. [PMID: 38689190 DOI: 10.1007/s00345-024-04995-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
PURPOSE To develop an early diagnosis model of prostate cancer based on clinical-radiomics to improve the accuracy of imaging diagnosis of prostate cancer. METHODS The multicenter study enrolled a total of 449 patients with prostate cancer from December 2017 to January 2022. We retrospectively collected information from 342 patients who underwent prostate biopsy at Minhang Hospital. We extracted T2WI images through 3D-Slice, and used mask tools to mark the prostate area manually. The radiomics features were extracted by Python using the "Pyradiomics" module. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for data dimensionality reduction and feature selection, and the radiomics score was calculated according to the correlation coefficients. Multivariate logistic regression analysis was used to develop predictive models. We incorporated the radiomics score, PI-RADS, and clinical features, and this was presented as a nomogram. The model was validated using a cohort of 107 patients from the Xuhui Hospital. RESULTS In total, 110 effective radiomics features were extracted. Finally, 9 features were significantly associated with the diagnosis of prostate cancer, from which we calculated the radiomics score. The predictors contained in the individualized prediction nomogram included age, fPSA/tPSA, PI-RADS, and radiomics score. The clinical-radiomics model showed good discrimination in the validation cohort (C-index = 0.88). CONCLUSION This study presents a clinical-radiomics model that incorporates age, fPSA/PSA, PI-RADS, and radiomics score, which can be conveniently used to facilitate individualized prediction of prostate cancer before prostate biopsy.
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Affiliation(s)
- Jiaqi Huang
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Chang He
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Peirong Xu
- Department of Urology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
- Department of Urology, Zhongshan Hospital, Fudan University, 180th Fengling Rd, Xuhui District, Shanghai, 200032, China
| | - Bin Song
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China
| | - Hainan Zhao
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China
| | - Bingde Yin
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Minke He
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Xuwei Lu
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Jiawen Wu
- Department of Urology, Minhang Hospital, Fudan University, Shanghai, China
| | - Hang Wang
- Department of Urology, Zhongshan Hospital, Fudan University, 180th Fengling Rd, Xuhui District, Shanghai, 200032, China.
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Krauss W, Frey J, Heydorn Lagerlöf J, Lidén M, Thunberg P. Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer. Acta Radiol 2024; 65:307-317. [PMID: 38115809 DOI: 10.1177/02841851231216555] [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] [Indexed: 12/21/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa. PURPOSE To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting. MATERIAL AND METHODS Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves. RESULTS In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171). CONCLUSION PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.
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Affiliation(s)
- Wolfgang Krauss
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Janusz Frey
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jakob Heydorn Lagerlöf
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Medical Physics, Karlstad Central Hospital, Sweden
| | - Mats Lidén
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Per Thunberg
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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Bao J, Qiao X, Song Y, Su Y, Ji L, Shen J, Yang G, Shen H, Wang X, Hu C. Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study. Insights Imaging 2024; 15:68. [PMID: 38424368 PMCID: PMC10904705 DOI: 10.1186/s13244-024-01631-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/28/2024] [Indexed: 03/02/2024] Open
Abstract
PURPOSE To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. MATERIAL AND METHODS A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. RESULTS The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001). CONCLUSIONS This radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists' PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further. CRITICAL RELEVANCE STATEMENT The radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans. KEY POINTS • The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists. • The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool. • The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.
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Affiliation(s)
- Jie Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Xiaomeng Qiao
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Yang Song
- Scientific Marketing, Siemens Healthineers, Shanghai 430#, Linqing Road, Shanghai, 201318, China
| | - Yueting Su
- Department of Radiology, The People's Hospital of Taizhou, 210#, Yingchun Road, Taizhou, Jiangsu, 225399, China
| | - Libiao Ji
- Department of Radiology, Changshu NO.1 People's Hospital, 1#, Shuyuan Street, Changshu, Jiangsu, 215501, China
| | - Junkang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, 1055#, Sanxiang Road, Suzhou, Jiangsu, 215004, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663#, Zhongshanbei Road, Shanghai, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, 118#, Wanshen Street, Suzhou, Jiangsu, 215028, China.
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, Jiangsu, 215006, China.
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, Jiangsu, 215006, China.
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Kaneko M, Magoulianitis V, Ramacciotti LS, Raman A, Paralkar D, Chen A, Chu TN, Yang Y, Xue J, Yang J, Liu J, Jadvar DS, Gill K, Cacciamani GE, Nikias CL, Duddalwar V, Jay Kuo CC, Gill IS, Abreu AL. The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics. Urol Clin North Am 2024; 51:1-13. [PMID: 37945095 DOI: 10.1016/j.ucl.2023.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution.
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Affiliation(s)
- Masatomo Kaneko
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer; Department of Urology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Vasileios Magoulianitis
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Lorenzo Storino Ramacciotti
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Alex Raman
- Western University of Health Sciences. Pomona, CA, USA
| | - Divyangi Paralkar
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Andrew Chen
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Timothy N Chu
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Yijing Yang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jintang Xue
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jiaxin Yang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jinyuan Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Donya S Jadvar
- Dornsife School of Letters and Science, University of Southern California, Los Angeles, CA, USA
| | - Karanvir Gill
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Giovanni E Cacciamani
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chrysostomos L Nikias
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - C-C Jay Kuo
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Inderbir S Gill
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andre Luis Abreu
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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Sun Z, Wu P, Cui Y, Liu X, Wang K, Gao G, Wang H, Zhang X, Wang X. Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI. J Magn Reson Imaging 2023; 58:1067-1081. [PMID: 36825823 DOI: 10.1002/jmri.28608] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. PURPOSE To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. STUDY TYPE Retrospective. POPULATION One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets. FIELD STRENGTH/SEQUENCE DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. ASSESSMENT The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. STATISTICAL TESTS The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. RESULTS The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). DATA CONCLUSION The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhaonan Sun
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China
| | - Yingpu Cui
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xiang Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Ge Gao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Huihui Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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7
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Weng X, Tan W, Wei B, Yang S, Gu C, Wang S. Interaction between drinking and dietary inflammatory index affects prostate specific antigen: a cross-sectional study. BMC Geriatr 2023; 23:537. [PMID: 37670257 PMCID: PMC10478225 DOI: 10.1186/s12877-023-04151-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 07/04/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Numerous studies have shown that the dietary inflammatory index (DII) is associated with adverse health effects. However, the relationship between DII and prostate cancer (PCa) remains controversial. Although alcohol is included in DII as a dietary factor, the various adverse health effects of alcohol consumption are not only related to inflammation. On the other hand, it has been a long-standing debate whether alcohol consumption is linked to the risk of PCa. Therefore, to clarify whether drinking affects the relationship between DII and PCa, we evaluated the correlation between DII and prostate-specific antigen (PSA) based on the National Health and Nutrition Examination Survey (NHANES) database. METHODS We used data from the NHANES spanning from 2005 to 2010 to analyze the relationship between PCa and DII. Out of the 31,034 NHANES participants, we enrolled 4,120 individuals in our study, utilizing dietary intake data from a twenty-four-hour period to determine DII scores. Demographic data, physical and laboratory test results were collected to compare between low PSA and high PSA groups, and to calculate the odds ratio between both groups, we employed a logistic regression analysis. RESULTS In this cross-sectional investigation of PCa, drinkers and non-drinkers had different relationships between DII and PSA levels (OR: 1.2, 95% Cl: 1-1.44 vs. OR: 0.98, 95% Cl: 0.9-1.07), and DII and abstaining from alcohol were effective in reducing the incidence of PSA (p-value for significant interaction = 0.037). CONCLUSION The results of our study suggest that drinking may influence the relationship between DII and PSA levels. DII is likely to be a reliable indicator for estimating PSA levels among non-drinkers, who may limit their intake of pro-inflammatory ingredients to lower the incidence and death of PCa.
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Affiliation(s)
- Xiangtao Weng
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Wenyue Tan
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Baian Wei
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shijian Yang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Chiming Gu
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
| | - Shusheng Wang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
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Calimano-Ramirez LF, Virarkar MK, Hernandez M, Ozdemir S, Kumar S, Gopireddy DR, Lall C, Balaji KC, Mete M, Gumus KZ. MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Abdom Radiol (NY) 2023; 48:2379-2400. [PMID: 37142824 DOI: 10.1007/s00261-023-03924-y] [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: 01/13/2023] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature. METHODS We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores. RESULTS We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science. CONCLUSION Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.
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Affiliation(s)
- Luis F Calimano-Ramirez
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Mayur K Virarkar
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Mauricio Hernandez
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Savas Ozdemir
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Sindhu Kumar
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Dheeraj R Gopireddy
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - K C Balaji
- Department of Urology, University of Florida College of Medicine, Jacksonville, FL, 32209, USA
| | - Mutlu Mete
- Department of Computer Science and Information System, Texas A&M University-Commerce, Commerce, TX, 75428, USA
| | - Kazim Z Gumus
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA.
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9
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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10
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Lei L, Du LX, He YL, Yuan JP, Wang P, Ye BL, Wang C, Hou Z. Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging. Front Oncol 2023; 13:1123493. [PMID: 37091168 PMCID: PMC10118007 DOI: 10.3389/fonc.2023.1123493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/17/2023] [Indexed: 04/09/2023] Open
Abstract
IntroductionThe successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature.MethodsThis study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary.Results and discussionThe experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.
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Affiliation(s)
- Lei Lei
- College of Information Science and Engineering, Jiaxing University, Jiaxing, China
- *Correspondence: Lei Lei, ; Ying-Long He,
| | - Li-Xin Du
- Medical Imaging Department, Shenzhen Longhua District Central Hospital, Shenzhen, China
| | - Ying-Long He
- School of Mechanical Engineering Sciences, University of Surrey, Guildford, United Kingdom
- *Correspondence: Lei Lei, ; Ying-Long He,
| | - Jian-Peng Yuan
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Pan Wang
- Medical Imaging Department, Shenzhen Longhua District Central Hospital, Shenzhen, China
| | - Bao-Lin Ye
- College of Information Science and Engineering, Jiaxing University, Jiaxing, China
| | - Cong Wang
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - ZuJun Hou
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
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11
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Li X, Li C, Chen M. Patients With "Gray Zone" PSA Levels: Application of Prostate MRI and MRS in the Diagnosis of Prostate Cancer. J Magn Reson Imaging 2023; 57:992-1010. [PMID: 36326563 DOI: 10.1002/jmri.28505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
Improving the detection rates of prostate cancer (PCa) and avoiding unnecessary prostate biopsies in men with prostate-specific antigen (PSA) levels within the gray zone require urgent attention. In this context, rapid advances in MR technology in recent years may offer a promising possibility. A systematic review to evaluate the applications of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in detecting PCa and clinically significant PCa (csPCa) in men with PSA levels within the gray zone. The study type is defined as systematic review. In July 2022, out of 229 studies identified by the database search and from other sources, 23 articles related to the selected topic of interest were included in this review. No field strength or sequence restrictions. The data including the study population, study characteristics, as well as basic MRI characteristics, from the final studies included in this review, were extracted independently by two reviewers. The major results of the original study were summarized and no additional statistical analysis was performed. Among the 23 studies included in this review, 17 focused on the applications of MRS and MRI for the prebiopsy diagnosis of PCa. Nine of these 17 articles used Prostate Imaging Reporting and Data System (PI-RADS) score to interpret MRI results, thereby confirming the practicality of the PI-RADS score in predicting PCa and csPCa. The remaining six articles evaluated the applications of MRI and MRS in guiding prostate biopsy. Although there was a variation in the biopsy modalities used in these studies, both MRI- and MRS-guided prostate biopsies were observed to improve the detection rates of PCa and csPCa in patients with PSA levels within the gray zone. MRS and MRI showed good performance in the detection of PCa and csPCa before biopsy. In addition, MRS- or MRI-guided prostate-targeted biopsies were able to improve the detection rates of PCa and csPCa. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xue Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chunmei Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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12
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Zhong JG, Shi L, Liu J, Cao F, Ma YQ, Zhang Y. Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance. Sci Rep 2023; 13:4846. [PMID: 36964192 PMCID: PMC10038986 DOI: 10.1038/s41598-023-31869-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T2-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.
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Affiliation(s)
- Jian-Guo Zhong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Shi
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jing Liu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Fang Cao
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yan-Qing Ma
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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13
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Chiacchio G, Castellani D, Nedbal C, De Stefano V, Brocca C, Tramanzoli P, Galosi AB, Donalisio da Silva R, Teoh JYC, Tiong HY, Naik N, Somani BK, Merseburger AS, Gauhar V. Radiomics vs radiologist in prostate cancer. Results from a systematic review. World J Urol 2023; 41:709-724. [PMID: 36867239 DOI: 10.1007/s00345-023-04305-2] [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: 08/26/2022] [Accepted: 01/20/2023] [Indexed: 03/04/2023] Open
Abstract
PURPOSE Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and extraprostatic extension in prostate cancer (PCa). METHODS The literature search was performed on June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only. RESULTS Seventeen papers were included. The combination of PIRADS and radiomics score models improves the PIRADS score reporting of 2 and 3 lesions even in the peripheral zone. Multiparametric MRI-based radiomics models suggest that by simply omitting diffusion contrast enhancement imaging in radiomics models can simplify the process of analysis of clinically significant PCa by PIRADS. Radiomics features correlated with the Gleason grade with excellent discriminative ability. Radiomics has higher accuracy in predicting not only the presence but also the side of extraprostatic extension. CONCLUSIONS Radiomics research on PCa mainly uses MRI as an imaging modality and is focused on diagnosis and risk stratification and has the best future possibility of improving PIRADS reporting. Radiomics has established its superiority over radiologist-reported outcomes but the variability has to be taken into consideration before translating it to clinical practice.
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Affiliation(s)
- Giuseppe Chiacchio
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Daniele Castellani
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy.
| | - Carlotta Nedbal
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Virgilio De Stefano
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Carlo Brocca
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Pietro Tramanzoli
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Andrea Benedetto Galosi
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | | | - Jeremy Yuen-Chun Teoh
- Department of Surgery, S.H.Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Ho Yee Tiong
- Department of Urology, National University Hospital, Singapore, Singapore
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K Somani
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK
| | - Axel S Merseburger
- Clinic of Urology, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Vineet Gauhar
- Department of Urology, Ng Teng Fong General Hospital, Singapore, Singapore
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14
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Canellas R, Kohli MD, Westphalen AC. The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging. Curr Oncol Rep 2023; 25:243-250. [PMID: 36749494 DOI: 10.1007/s11912-023-01371-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging. RECENT FINDINGS Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.
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Affiliation(s)
- Rodrigo Canellas
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
| | - Marc D Kohli
- Clinical Informatics, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA.,Imaging Informatics, UCSF Health, 500 Parnassus Ave, 3rd Floor, San Francisco, CA, 94143, USA
| | - Antonio C Westphalen
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA. .,Department of Urology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA. .,Department Radiation Oncology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA.
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15
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Zhang CC, Tu X, Lin TH, Cai DM, Yang L, Qiu S, Liu ZH, Yang L, Wei Q. Combining clinical parameters and multiparametric magnetic resonance imaging to stratify biopsy-naïve men for an optimum diagnostic strategy with prostate-specific antigen 4 ng ml -1 to 10 ng ml -1. Asian J Androl 2023; 25:492-498. [PMID: 36571328 PMCID: PMC10411252 DOI: 10.4103/aja202288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 10/25/2022] [Indexed: 12/27/2022] Open
Abstract
We attempted to perform risk categories based on the free/total prostate-specific antigen ratio (%fPSA), prostate-specific antigen (PSA) density (PSAD, in ng ml-2), and multiparametric magnetic resonance imaging (mpMRI) step by step, with the goal of determining the best clinical diagnostic strategy to avoid unnecessary tests and prostate biopsy (PBx) in biopsy-naïve men with PSA levels ranging from 4 ng ml-1 to 10 ng ml-1. We included 439 patients who had mpMRI and PBx between August 2018 and July 2021 (West China Hospital, Chengdu, China). To detect clinically significant prostate cancer (csPCa) on PBx, receiver-operating characteristic (ROC) curves and their respective area under the curve were calculated. Based on %fPSA, PSAD, and Prostate Imaging-Reporting and Data System (PI-RADS) scores, the negative predictive value (NPV) and positive predictive value (PPV) were calculated sequentially. The optimal %fPSA threshold was determined to be 0.16, and the optimal PSAD threshold was 0.12 for %fPSA ≥0.16 and 0.23 for %fPSA <0.16, respectively. When PSAD <0.12 was combined with patients with %fPSA ≥0.16, the NPV of csPCa increased from 0.832 (95% confidence interval [CI]: 0.766-0.887) to 0.931 (95% CI: 0.833-0.981); the detection rate of csPCa was similar when further stratified by PI-RADS scores (P = 0.552). Combining %fPSA <0.16 with PSAD ≥0.23 ng ml-2 predicted significantly more csPCa patients than those with PSAD <0.23 ng ml-2 (58.4% vs 26.7%, P < 0.001). Using PI-RADS scores 4 and 5, the PPV was 0.739 (95% CI: 0.634-0.827) when further stratified by mpMRI results. In biopsy-naïve patients with PSA level of 4-10 ng ml-1, stratification of %fPSA and PSAD combined with PI-RADS scores may be useful in the decision-making process prior to undergoing PBx.
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Affiliation(s)
- Chi-Chen Zhang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiang Tu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tian-Hai Lin
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Di-Ming Cai
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Ling Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Shi Qiu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhen-Hua Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lu Yang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
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16
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Du L, Yuan J, Gan M, Li Z, Wang P, Hou Z, Wang C. A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images. BMC Med Imaging 2022; 22:218. [DOI: 10.1186/s12880-022-00946-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
Abstract
Purpose
To compare a deep learning model with a radiomics model in differentiating high-grade (LR-3, LR-4, LR-5) liver imaging reporting and data system (LI-RADS) liver tumors from low-grade (LR-1, LR-2) LI-RADS tumors based on the contrast-enhanced magnetic resonance images.
Methods
Magnetic resonance imaging scans of 361 suspected hepatocellular carcinoma patients were retrospectively reviewed. Lesion volume segmentation was manually performed by two radiologists, resulting in 426 lesions from the training set and 83 lesions from the test set. The radiomics model was constructed using a support vector machine (SVM) with pre-defined features, which was first selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The deep learning model was established based on the DenseNet. Performance of the models was quantified by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score.
Results
A set of 8 most informative features was selected from 1049 features to train the SVM classifier. The AUCs of the radiomics model were 0.857 (95% confidence interval [CI] 0.816–0.888) for the training set and 0.879 (95% CI 0.779–0.935) for the test set. The deep learning method achieved AUCs of 0.838 (95% CI 0.799–0.871) for the training set and 0.717 (95% CI 0.601–0.814) for the test set. The performance difference between these two models was assessed by t-test, which showed the results in both training and test sets were statistically significant.
Conclusion
The deep learning based model can be trained end-to-end with little extra domain knowledge, while the radiomics model requires complex feature selection. However, this process makes the radiomics model achieve better performance in this study with smaller computational cost and more potential on model interpretability.
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Sun H, Du F, Liu Y, Li Q, Liu X, Wang T. DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml. Front Oncol 2022; 12:925186. [PMID: 36578948 PMCID: PMC9792168 DOI: 10.3389/fonc.2022.925186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
Background This study investigated the diagnostic utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters for distinguishing between benign and malignant prostate tumors when serum prostate-specific antigen (PSA) level is ≥10 ng/ml. Methods Patients with prostate cancer (PCa) and benign prostatic hyperplasia (BPH) with serum PSA ≥10 ng/ml before treatment were recruited. Transrectal ultrasound-guided biopsy or surgery was performed for tumor classification and patients were stratified accordingly into PCa and BPH groups. Patients underwent DCE-MRI and DWI scanning and the transfer constant (Ktrans), rate constant (Kep), fractional volume of the extravascular extracellular space, plasma volume (Vp), and Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) score were determined. The apparent diffusion coefficient (ADC) was calculated from DWI. The diagnostic performance of these parameters was assessed by receiver operating characteristic (ROC) curve analysis, and those showing a significant difference between the PCa and BPH groups were combined into a multivariate logistic regression model for PCa diagnosis. Spearman's correlation was used to analyze the relationship between Gleason score and imaging parameters. Results The study enrolled 65 patients including 32 with PCa and 33 with BPH. Ktrans (P=0.006), Kep (P=0.001), and Vp (P=0.009) from DCE-MRI and ADC (P<0.001) from DWI could distinguish between the 2 groups when PSA was ≥10 ng/ml. PI-RADS score (area under the ROC curve [AUC]=0.705), Ktrans (AUC=0.700), Kep (AUC=0.737), Vp (AUC=0.688), and ADC (AUC=0.999) showed high diagnostic performance for discriminating PCa from BPH. A combined model based on PI-RADS score, Ktrans, Kep, Vp, and ADC had a higher AUC (1.000), with a sensitivity of 0.998 and specificity of 0.999. Imaging markers showed no significant correlation with Gleason score in PCa. Conclusion DCE-MRI and DWI parameters can distinguish between benign and malignant prostate tumors in patients with serum PSA ≥10 ng/ml.
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Affiliation(s)
- Hongmei Sun
- Department of Magenetic Resonance Imaging (MRI), Henan Province Hospital of Traditional Chinese Medicine (The Second Affiliated Hospital of Henan University of Chinese Medicine), Zhengzhou, China,*Correspondence: Hongmei Sun,
| | - Fengli Du
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Yan Liu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
| | - Qian Li
- Department of Magenetic Resonance Imaging (MRI), Henan Province Hospital of Traditional Chinese Medicine (The Second Affiliated Hospital of Henan University of Chinese Medicine), Zhengzhou, China
| | - Xinai Liu
- Department of Magenetic Resonance Imaging (MRI), Henan Province Hospital of Traditional Chinese Medicine (The Second Affiliated Hospital of Henan University of Chinese Medicine), Zhengzhou, China
| | - Tongming Wang
- Department of Magenetic Resonance Imaging (MRI), Henan Province Hospital of Traditional Chinese Medicine (The Second Affiliated Hospital of Henan University of Chinese Medicine), Zhengzhou, China
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Lu Y, Li B, Huang H, Leng Q, Wang Q, Zhong R, Huang Y, Li C, Yuan R, Zhang Y. Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL. Front Oncol 2022; 12:1020317. [PMID: 36582803 PMCID: PMC9793773 DOI: 10.3389/fonc.2022.1020317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/21/2022] [Indexed: 12/07/2022] Open
Abstract
Purpose To investigate the predictive performance of the combined model by integrating clinical variables and radiomic features for the accurate detection of prostate cancer (PCa) in patients with prostate-specific antigen (PSA) serum levels of 4-10 ng/mL. Methods A retrospective study of 136 males (mean age, 67.3 ± 8.4 years) with Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 category ≤3 lesions and PSA serum levels of 4-10 ng/mL were performed. All patients underwent multiparametric MRI at 3.0T and transrectal ultrasound-guided systematic prostate biopsy in their clinical workup. Radiomic features were extracted from axial T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps of each patient using PyRadiomics. Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were implemented to identify the most significant radiomic features. Independent clinic-radiological factors were identified via univariate and multivariate regression analyses. Seven machine-learning algorithms were compared to construct a single-layered radiomic score (ie, radscore) and multivariate regression analysis was applied to construct the fusion radscore. Finally, the radiomic nomogram was further developed by integrating useful clinic-radiological factors and fusion radscore using multivariate regression analysis. The discriminative power of the nomogram was evaluated by area under the curve (AUC), DeLong test, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results The transitional zone-specific antigen density was identified as the only independent clinic-radiological factor, which yielded an AUC of 0.592 (95% confidence interval [CI]: 0.527-0.657). The ADC radscore based on six features and Naive Bayes achieved an AUC of 0.779 (95%CI: 0.730-0.828); the T2WI radscore based on 13 features and Support Vector Machine yielded an AUC of 0.808 (95%CI: 0.761-0.855). The fusion radscore obtained an improved AUC of 0.844 (95%CI: 0.801-0.887), which was higher than the single-layered radscores (both P<0.05). The radiomic nomogram achieved the highest value among all models (all P<0.05), with an AUC of 0.872 (95%CI: 0.835-0.909). Calibration curve showed good agreement and DCA together with CIC confirmed the clinical benefits of the radiomic nomogram. Conclusion The radiomic nomogram holds the potential for accurate and noninvasive identification of PCa in patients with PI-RADS ≤3 lesions and PSA of 4-10 ng/mL, which could reduce unnecessary biopsy.
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Affiliation(s)
- Yangbai Lu
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Binfei Li
- Department of Anesthesiology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Hongxing Huang
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Qu Leng
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Qiang Wang
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Rui Zhong
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Yaqiang Huang
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Canyong Li
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Runqiang Yuan
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China,*Correspondence: Yongxin Zhang, ; Runqiang Yuan,
| | - Yongxin Zhang
- Department of Magnetic Resonance Imaging, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China,*Correspondence: Yongxin Zhang, ; Runqiang Yuan,
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MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies. Diagnostics (Basel) 2022; 12:diagnostics12123005. [PMID: 36553012 PMCID: PMC9776817 DOI: 10.3390/diagnostics12123005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/04/2022] Open
Abstract
OBJECTIVE The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters. METHODS In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. RESULTS The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. CONCLUSION The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.
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Knight AS, Sharma P, de Riese WTW. MRI determined prostate volume and the incidence of prostate cancer on MRI-fusion biopsy: a systemic review of reported data for the last 20 years. Int Urol Nephrol 2022; 54:3047-3054. [PMID: 36040649 DOI: 10.1007/s11255-022-03351-w] [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: 07/13/2022] [Accepted: 08/20/2022] [Indexed: 10/14/2022]
Abstract
PURPOSE Magnetic resonance imaging (MRI) is a precise, systemic and advantageous imaging technique when compared to transrectal ultrasound (TRUS) which is very operator dependent. The negative correlation between prostate volume and the incidence of prostate cancer (PCa) obtained by TRUS biopsy has been well documented in the literature. The purpose of this systemic review is analyzing the reported MRI-fusion study results on prostate biopsies regarding any correlation between prostate volume and the incidence of PCa. METHODS After defining the inclusion and exclusion criteria an in-depth review were performed between 01.01.2000 and 02.08.2022 using the PubMed database and applying the "PRISMA" guidelines. RESULTS Twelve studies qualified, and all showed an inverse/negative relationship between prostate volume and incidence of PCa. Sample sizes ranged from 33 to 2767 patients in single and multi-institutional studies. All studies showed a statistically significant inverse relationship with a p value < 0.05. The graph summarizing all of studies and using Fisher's method revealed a highly significant combined p level of 0.00001. Additionally, not one single study was found showing the contrary (a positive correlation between prostate size and the incidence of PCa). CONCLUSION To our knowledge, this is the first systemic review of reported MRI-Fusion data on the incidence of PCa in correlation with prostate volume. This MRI review confirms previous TRUS-biopsy studies which demonstrated an inverse relationship between prostate volume and the incidence of PCa, and thus further supports the hypothesis that large prostates size may be protective against PCa when compared to smaller prostates.
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Affiliation(s)
- Andrew S Knight
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, 3601-4th Street STOP 7260, Lubbock, TX, 79430-7260, USA
| | - Pranav Sharma
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, 3601-4th Street STOP 7260, Lubbock, TX, 79430-7260, USA
| | - Werner T W de Riese
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, 3601-4th Street STOP 7260, Lubbock, TX, 79430-7260, USA.
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21
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Lei Y, Li TJ, Gu P, Yang YK, Zhao L, Gao C, Hu J, Liu XD. Combining prostate-specific antigen density with prostate imaging reporting and data system score version 2.1 to improve detection of clinically significant prostate cancer: A retrospective study. Front Oncol 2022; 12:992032. [PMID: 36212411 PMCID: PMC9539128 DOI: 10.3389/fonc.2022.992032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/15/2022] [Indexed: 12/24/2022] Open
Abstract
Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of <0.15 ng/mL2, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer.
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Affiliation(s)
- Yin Lei
- Department of Urology, The First People’s Hospital of Shuangliu District, Chengdu, China
| | - Tian Jie Li
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Peng Gu
- Department of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yu kun Yang
- Medical school, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Zhao
- Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chao Gao
- Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Juan Hu
- Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, China
- *Correspondence: Xiao Dong Liu, ; Juan Hu,
| | - Xiao Dong Liu
- Department of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
- *Correspondence: Xiao Dong Liu, ; Juan Hu,
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22
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Li L, Gu L, Kang B, Yang J, Wu Y, Liu H, Lai S, Wu X, Jiang J. Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions. Front Oncol 2022; 12:934108. [PMID: 35865467 PMCID: PMC9295912 DOI: 10.3389/fonc.2022.934108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/07/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo compare the performance of different imaging classifiers in the prospective diagnosis of prostate diseases based on multiparameter MRI.MethodsA total of 238 patients with pathological outcomes were enrolled from September 2019 to July 2021, including 142 in the training set and 96 in the test set. After the regions of interest were manually segmented, decision tree (DT), Gaussian naive Bayes (GNB), XGBoost, logistic regression, random forest (RF) and support vector machine classifier (SVC) models were established on the training set and tested on the independent test set. The prospective diagnostic performance of each classifier was compared by using the AUC, F1-score and Brier score.ResultsIn the patient-based data set, the top three classifiers of combined sequences in terms of the AUC were logistic regression (0.865), RF (0.862), and DT (0.852); RF “was significantly different from the other two classifiers (P =0.022, P =0.005), while logistic regression and DT had no statistical significance (P =0.802). In the lesions-based data set, the top three classifiers of combined sequences in terms of the AUC were RF (0.931), logistic regression (0.922) and GNB (0.922). These three classifiers were significantly different from.ConclusionThe results of this experiment show that radiomics has a high diagnostic efficiency for prostate lesions. The RF classifier generally performed better overall than the other classifiers in the experiment. The XGBoost and logistic regression models also had high classification value in the lesions-based data set.
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Affiliation(s)
- Linghao Li
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Lili Gu
- Department of Pain, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Bin Kang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jiaojiao Yang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Ying Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Hao Liu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Shasha Lai
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Xueting Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jian Jiang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
- *Correspondence: Jian Jiang,
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23
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PSA density is complementary to prostate MP-MRI PI-RADS scoring system for risk stratification of clinically significant prostate cancer. Prostate Cancer Prostatic Dis 2022:10.1038/s41391-022-00549-y. [PMID: 35523940 DOI: 10.1038/s41391-022-00549-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/07/2022] [Accepted: 04/22/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND While prostate multiparametric-magnetic resonance imaging (MP-MRI) has improved the diagnosis of clinically significant prostate cancer (CSPC), the complementary use of prostate-specific antigen (PSA) levels to risk-stratify for CSPC requires further study. The objective of this project was to determine if prostate MP-MRI and PSA can provide complementary insights into CSPC risk stratification. METHODS In an IRB-approved study, pathologic outcomes from patients who underwent MR/US fusion-targeted prostate biopsy were stratified by various parameters including PSA, PSA density (PSAD), age, race, and PI-RADS v2 score. CSPC was defined as a Gleason score ≥7. Logistic regression was used to determine odds ratios (OR) with 95% confidence intervals (CI). P values were reported as two-sided with p < 0.05 considered statistically significant. ROC curves were generated for assessing the predictive value of tests and sensitivity + specificity optimization was performed to determine optimal testing cutoffs. RESULTS A total of 327 patients with 709 lesions total were analyzed. PSAD and PI-RADS scores provided complementary predictive value for diagnosis of CSPC (AUC PSAD: 0.67, PI-RADS: 0.72, combined: 0.78, p < 0.001). When controlling for PI-RADS score, age, and race, multivariate analysis showed that PSAD was independently associated with CSPC (OR 1.03 per 0.01 PSAD increase, 95% CI 1.02-105, p < 0.001). The optimal cutoff of PSAD ≥ 0.1 ng/ml/cc shows that a high versus low PSAD was roughly equivalent to an increase in 1 in PI-RADS score for the presence of CSPC (4% of PI-RADS ≤3 PSAD low, 6% of PI-RADS 3 PSAD high vs. 5% of PI-RADS 4 PSAD low, 22% of PI-RADS 4 PSAD high vs. 29% of PI-RADS 5 PSAD low, 46% of PI-RADS 5 PSAD high were found to have CSPC). CONCLUSIONS PSAD with a cutoff of 0.1 ng/ml/cc appears to be a useful marker that can stratify the risk of CSPC in a complementary manner to prostate MP-MRI.
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Zhang L, Jiang D, Chen C, Yang X, Lei H, Kang Z, Huang H, Pang J. Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer. Br J Radiol 2022; 95:20210191. [PMID: 34289319 PMCID: PMC8978240 DOI: 10.1259/bjr.20210191] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To develop and validate a non-invasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. METHODS In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and diffusion-weighted imaging (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. RESULTS Nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. CONCLUSION The multiparametric MRI-based radiomics signature can potentially serve as a non-invasive tool for distinguishing between indolent and aggressive PCa prior to therapy. ADVANCES IN KNOWLEDGE The multiparametric MRI-based radiomics signature has the potential to non-invasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.
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Affiliation(s)
| | - Donggen Jiang
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center,The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Chujie Chen
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center,The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Xiangwei Yang
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center,The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hanqi Lei
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center,The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hai Huang
- Department of Urology, The Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jun Pang
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center,The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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MRI-based radiomics model for distinguishing endometrial carcinoma from benign mimics: A multicenter study. Eur J Radiol 2021; 146:110072. [PMID: 34861530 DOI: 10.1016/j.ejrad.2021.110072] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/19/2021] [Accepted: 11/22/2021] [Indexed: 12/30/2022]
Abstract
PURPOSE To develop and validate an MRI-based radiomics model for preoperatively distinguishing endometrial carcinoma (EC) with benign mimics in a multicenter setting. METHODS Preoperative MRI scans of EC patients were retrospectively reviewed and divided into training set (158 patients from device 1 in center A), test set #1 (78 patients from device 2 in center A) and test set #2 (109 patients from device 3 in center B). Two radiologists performed manual delineation of tumor segmentation on the map of apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI). The features were extracted and firstly selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The support vector machine (SVM) was employed to build the radiomics model, which is tuned in the training set using 10-fold cross-validation, and then assessed on the test set. Performance of the model was determined by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score. RESULTS Five most informative features are selected from the extracted 3142 features. The SVM with linear kernel was employed to build the radiomics model. The AUCs of the model were 0.989 (95% confidence interval [CI]: 0.968-0.997) for the training set, 0.999 (95% CI: 0.991-1.000) for test set #1, 0.961 (95% CI: 0.902-0.983) for test set #2. Accuracies of the model were 0.937 for the training set (precision: 0.919, recall: 0.963, F1-score: 0.940), 0.974 for test set #1 (precision: 0.949, recall: 1.000, F1-score: 0.974) and 0.908 for test set #2 (precision: 0.899, recall: 0.954, F1-score: 0.925). These results confirmed the efficacy of this model in predicting EC in different centers. CONCLUSION The MRI-based radiomics model showed promising potential for distinguishing EC with benign mimics and might be useful for surgical management of EC.
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Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, Scifo P, Picchio M. State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol 2021; 169:103544. [PMID: 34801699 DOI: 10.1016/j.critrevonc.2021.103544] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/18/2021] [Accepted: 10/18/2021] [Indexed: 02/04/2023] Open
Abstract
We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.
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Affiliation(s)
| | | | - Luca Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentino Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annarita Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ilaria Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Erik Preza
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy; Radiology Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Semen as a rich source of diagnostic biomarkers for prostate cancer: latest evidence and implications. Mol Cell Biochem 2021; 477:213-223. [PMID: 34655417 DOI: 10.1007/s11010-021-04273-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/01/2021] [Indexed: 12/24/2022]
Abstract
Prostate cancer (PCa) is one of the most common cancers in men and the cause of numerous cancer deaths in the world. Nowadays, based on diagnostic criteria, prostate-specific antigen (PSA) evaluation and rectal examination are used to diagnose prostate-related malignancies. However, due to the different types of PCa, there are several doubts about the diagnostic value of PSA. On the other hand, semen is considered an appropriate source and contains various biomarkers in non-invasive diagnosing several autoimmune disorders and malignancies. Evidence suggests that analysis of semen biomarkers could be helpful in PCa diagnosis. Therefore, due to the invasiveness of most diagnostic methods in PCa, the use of semen as a biologic sample containing various biomarkers can lead to the emergence of novel and non-invasive diagnostic approaches. This review summarized recent studies on the use of various seminal biomarkers for diagnosis, prognosis and prediction of PCa.
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Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021; 22:ijms22189971. [PMID: 34576134 PMCID: PMC8465891 DOI: 10.3390/ijms22189971] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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Abstract
PURPOSE OF REVIEW The purpose of this review was to identify the most recent lines of research focusing on the application of artificial intelligence (AI) in the diagnosis and staging of prostate cancer (PCa) with imaging. RECENT FINDINGS The majority of studies focused on the improvement in the interpretation of bi-parametric and multiparametric magnetic resonance imaging, and in the planning of image guided biopsy. These initial studies showed that AI methods based on convolutional neural networks could achieve a diagnostic performance close to that of radiologists. In addition, these methods could improve segmentation and reduce inter-reader variability. Methods based on both clinical and imaging findings could help in the identification of high-grade PCa and more aggressive disease, thus guiding treatment decisions. Though these initial results are promising, only few studies addressed the repeatability and reproducibility of the investigated AI tools. Further, large-scale validation studies are missing and no diagnostic phase III or higher studies proving improved outcomes regarding clinical decision making have been conducted. SUMMARY AI techniques have the potential to significantly improve and simplify diagnosis, risk stratification and staging of PCa. Larger studies with a focus on quality standards are needed to allow a widespread introduction of AI in clinical practice.
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Affiliation(s)
- Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Eid M, Iannicelli E, Laghi A. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications. Cancers (Basel) 2021; 13:cancers13112681. [PMID: 34072366 PMCID: PMC8197789 DOI: 10.3390/cancers13112681] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine. Abstract Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marwen Eid
- Internal Medicine, Northwell Health Staten Island University Hospital, Staten Island, New York, NY 10305, USA;
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-0633775285
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Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11060959. [PMID: 34073627 PMCID: PMC8229869 DOI: 10.3390/diagnostics11060959] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022] Open
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
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Bai H, Xia W, Ji X, He D, Zhao X, Bao J, Zhou J, Wei X, Huang Y, Li Q, Gao X. Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer. J Magn Reson Imaging 2021; 54:1222-1230. [PMID: 33970517 DOI: 10.1002/jmri.27678] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 04/17/2021] [Accepted: 04/19/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Preoperative prediction of extracapsular extension (ECE) of prostate cancer (PCa) is important to guide clinical decision-making and improve patient prognosis. PURPOSE To investigate the value of multiparametric magnetic resonance imaging (mpMRI)-based peritumoral radiomics for preoperative prediction of the presence of ECE. STUDY TYPE Retrospective. POPULATION Two hundred eighty-four patients with PCa from two centers (center 1: 226 patients; center 2: 58 patients). Cases from center 1 were randomly divided into training (158 patients) and internal validation (68 patients) sets. Cases from center 2 were assigned to the external validation set. FIELD STRENGTH/SEQUENCE A 3.0 T MRI scanners (three vendors). Sequence: Pelvic T2-weighted turbo/fast spin echo sequence and diffusion weighted echo planar imaging sequence. ASSESSMENT The peritumoral region (PTR) was obtained by 3-12 mm (half of the tumor length) 3D dilatation of the intratumoral region (ITR). Single-MRI radiomics signatures, mpMRI radiomics signatures, and integrated models, which combined clinical characteristics with the radiomics signatures were built. The discrimination ability was assessed by area under the receiver operating characteristic curve (AUC) in the internal and external validation sets. STATISTICAL TESTS Fisher's exact test, Mann-Whitney U-test, DeLong test. RESULTS The PTR radiomics signatures demonstrated significantly better performance than the corresponding ITR radiomics signatures (AUC: 0.674 vs. 0.554, P < 0.05 on T2-weighted, 0.652 vs. 0.546, P < 0.05 on apparent diffusion coefficient, 0.682 vs. 0.556 on mpMRI in the external validation set). The integrated models combining the PTR radiomics signature with clinical characteristics performed better than corresponding radiomics signatures in the internal validation set (eg. AUC: 0.718 vs. 0.671, P < 0.05 on mpMRI) but performed similar in the external validation set (eg. AUC: 0.684, vs. 0.682, P = 0.45 on mpMRI). DATA CONCLUSION The peritumoral radiomics can better predict the presence of ECE preoperatively compared with the intratumoral radiomics and may have better generalization than clinical characteristics. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Honglin Bai
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Wei Xia
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xuefu Ji
- The School of Electro-Optical Engineering, Changchun University of Science and Technology, Changchun, 130013, China
| | - Dong He
- Department of Urology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Xingyu Zhao
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Jie Bao
- Department of Radiology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Jian Zhou
- Department of Radiology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of SooChow University, Suzhou, 215006, China
| | - Qiong Li
- Department of Radiology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xin Gao
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.,Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
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He J, Li C, Ye J, Qiao Y, Gu L. Self-speculation of clinical features based on knowledge distillation for accurate ocular disease classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102491] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Wang J, Liu X, Hu B, Gao Y, Chen J, Li J. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy. Abdom Radiol (NY) 2021; 46:1805-1815. [PMID: 33151359 DOI: 10.1007/s00261-020-02846-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/25/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE In the clinical management of patients with locally advanced rectal cancer (LARC), the early identification of poor and good responders after neoadjuvant chemoradiotherapy (N-CRT) is essential. Therefore, we developed and validated predictive models including MRI findings from the structured report template, clinical and radiomics parameters to differentiate between poor and good responders in patients with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy. METHODS Preoperative multiparametric MRI from 183 patients with locally advanced rectal cancer (122 in the training cohort, 61 in the validation cohort) was included in this retrospective study. After preprocessing, radiomic features were extracted and two methods of feature selection was applied to reduce the number of radiomics features. Logistic regression (LR) and random forest (RF) machine learning classifiers were trained to identify good responders from poor responders. Multivariable logistic regression analysis was used to incorporate the radiomic signature and clinical risk factors into a nomogram. Classifier performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS For the differentiation of poor and good responders, the radiomics model with an LR classifier achieved AUCs of 0.869 and 0.842 for the training and validation cohorts, respectively. The nomogram showed the highest reproducibility and prognostic ability in the training and validation cohorts, with AUCs of 0.923 (95% confidence interval, 0.872-0.975) and 0.898 (0.819-0.978), respectively. Additionally, the nomogram achieved significant risk stratification of patients in respect to progression free survival (PFS). CONCLUSIONS The nomogram accurately differentiated good and poor responders in patients with LARC undergoing N-CRT, and showed significant performance for predicting PFS.
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Affiliation(s)
- Jia Wang
- Department of Ultrasound, Qingdao Women and Children Hospital, Qingdao, Shandong, China
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Bin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Jingjing Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China.
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Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021; 23:e22394. [PMID: 33792552 PMCID: PMC8050752 DOI: 10.2196/22394] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 11/26/2020] [Accepted: 01/17/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. OBJECTIVE This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. RESULTS In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. CONCLUSIONS The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.
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Han C, Ma S, Liu X, Liu Y, Li C, Zhang Y, Zhang X, Wang X. Radiomics Models Based on Apparent Diffusion Coefficient Maps for the Prediction of High‐Grade Prostate Cancer at Radical Prostatectomy: Comparison With Preoperative Biopsy. J Magn Reson Imaging 2021; 54:1892-1901. [PMID: 33682286 DOI: 10.1002/jmri.27565] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 02/01/2021] [Accepted: 02/02/2021] [Indexed: 12/18/2022] Open
Affiliation(s)
- Chao Han
- Department of Radiology Peking University First Hospital Beijing China
| | - Shuai Ma
- Department of Radiology Peking University First Hospital Beijing China
| | - Xiang Liu
- Department of Radiology Peking University First Hospital Beijing China
| | - Yi Liu
- Department of Radiology Peking University First Hospital Beijing China
| | - Changxin Li
- Beijing Smart Tree Medical Technology Co. Ltd. Beijing China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd. Beijing China
| | - Xiaodong Zhang
- Department of Radiology Peking University First Hospital Beijing China
| | - Xiaoying Wang
- Department of Radiology Peking University First Hospital Beijing China
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Shi Y, Wahle E, Du Q, Krajewski L, Liang X, Zhou S, Zhang C, Baine M, Zheng D. Associations between Statin/Omega3 Usage and MRI-Based Radiomics Signatures in Prostate Cancer. Diagnostics (Basel) 2021; 11:diagnostics11010085. [PMID: 33430275 PMCID: PMC7825695 DOI: 10.3390/diagnostics11010085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/31/2020] [Accepted: 01/05/2021] [Indexed: 12/19/2022] Open
Abstract
Prostate cancer is the most common noncutaneous cancer and the second leading cause of cancer deaths among American men. Statins and omega-3 are two medications recently found to correlate with prostate cancer risk and aggressiveness, but the observed associations are complex and controversial. We therefore explore the novel application of radiomics in studying statin and omega-3 usage in prostate cancer patients. On MRIs of 91 prostate cancer patients, two regions of interest (ROIs), the whole prostate and the peripheral region of the prostate, were manually segmented. From each ROI, 944 radiomic features were extracted after field bias correction and normalization. Heatmaps were generated to study the radiomic feature patterns against statin or omega-3 usage. Radiomics models were trained on selected features and evaluated with 500-round threefold cross-validation for each drug/ROI combination. On the 1500 validation datasets, the radiomics model achieved average AUCs of 0.70, 0.74, 0.78, and 0.72 for omega-3/prostate, omega-3/peripheral, statin/prostate, and statin/peripheral, respectively. As the first study to analyze radiomics in relation to statin and omega-3 uses in prostate cancer patients, our study preliminarily established the existence of imaging-identifiable tissue-level changes in the prostate and illustrated the potential usefulness of radiomics for further exploring these medications’ effects and mechanisms in prostate cancer.
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Affiliation(s)
- Yu Shi
- Department of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA; (Y.S.); (Q.D.)
| | - Ethan Wahle
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68189, USA; (E.W.); (L.K.); (S.Z.)
| | - Qian Du
- Department of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA; (Y.S.); (Q.D.)
| | - Luke Krajewski
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68189, USA; (E.W.); (L.K.); (S.Z.)
| | - Xiaoying Liang
- Department of Radiation Oncology, University of Florida Proton Institute, Jacksonville, FL 32206, USA;
| | - Sumin Zhou
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68189, USA; (E.W.); (L.K.); (S.Z.)
| | - Chi Zhang
- Department of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA; (Y.S.); (Q.D.)
- Correspondence: (C.Z.); (M.B.); (D.Z.)
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68189, USA; (E.W.); (L.K.); (S.Z.)
- Correspondence: (C.Z.); (M.B.); (D.Z.)
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68189, USA; (E.W.); (L.K.); (S.Z.)
- Correspondence: (C.Z.); (M.B.); (D.Z.)
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Chen Y, Ruan M, Zhou B, Hu X, Wang H, Liu H, Liu J, Song G. Cutoff Values of Prostate Imaging Reporting and Data System Version 2.1 Score in Men With Prostate-specific Antigen Level 4 to 10 ng/mL: Importance of Lesion Location. Clin Genitourin Cancer 2021; 19:288-295. [PMID: 33632569 DOI: 10.1016/j.clgc.2020.12.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/18/2020] [Accepted: 12/26/2020] [Indexed: 10/22/2022]
Abstract
INTRODUCTION Multiparametric magnetic resonance imaging (mpMRI) has been shown to have a good performance in predicting cancer among patients with a prostate-specific antigen (PSA) level of 4 to 10 ng/mL. However, lesion location on mpMRI has never been separately considered. PATIENTS AND METHODS Patients with PSA level of 4 to 10 ng/mL were prospectively enrolled and underwent transrectal ultrasound-guided prostate biopsy. Patient information was collected, and logistic regression analysis was performed to determine the predictive factors of clinically significant prostate cancer (csPCa). Patients were grouped by lesion location to determine the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 cutoff value in predicting csPCa. RESULTS Among 222 patients, 121 were diagnosed with PCa and 92 had csPCa. Age, prostate volume, PSA density, location (peripheral zone, csPCa only), and PI-RADS v2.1 score were correlated with PCa and csPCa, and PI-RADS v2.1 score was the best predictor. A PI-RADS v2.1 score of 4 was the best cutoff value for predicting csPCa in patients with lesions only in the transitional zone with respect to the Youden index (0.5896) and negative predictive value (93.10%) with acceptable sensitivity (81.82%) and specificity (77.14%). An adjustment of the cutoff value to 3 for lesions in the peripheral zone would increase the negative predictive value (92.00%) and decrease the false negative rate (2.90%) with an acceptable sensitivity (97.10%) and specificity (30.67%). CONCLUSION PI-RADS v2.1 score is an effective predictor of csPCa in patients with PSA levels of 4 to 10 ng/mL. Patients with transitional zone or peripheral zone lesions should undergo biopsy if the PI-RADS v2.1 score is ≥ 4 or ≥ 3, respectively.
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Affiliation(s)
- Yuanchong Chen
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, Beijing, China; National Urological Cancer Center of China, Beijing, China
| | - Mingjian Ruan
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, Beijing, China; National Urological Cancer Center of China, Beijing, China
| | - Binyi Zhou
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, Beijing, China; National Urological Cancer Center of China, Beijing, China
| | - Xuege Hu
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, Beijing, China; National Urological Cancer Center of China, Beijing, China
| | - Hao Wang
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, Beijing, China; National Urological Cancer Center of China, Beijing, China
| | - Hua Liu
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, Beijing, China; National Urological Cancer Center of China, Beijing, China
| | - Jia Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Gang Song
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, Beijing, China; National Urological Cancer Center of China, Beijing, China.
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Leech M, Osman S, Jain S, Marignol L. Mini review: Personalization of the radiation therapy management of prostate cancer using MRI-based radiomics. Cancer Lett 2020; 498:210-216. [PMID: 33160001 DOI: 10.1016/j.canlet.2020.10.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 12/21/2022]
Abstract
Decisions on how to treat prostate cancer with radiation therapy are guideline-based but as such guidelines have been developed for populations of patients, this invariably leads to overly aggressive treatment in some patients and insufficient treatment in others. Heterogeneity within prostate tumors and in metastatic sites, even within the same patient, is believed to be a major cause of treatment failure. Radiomics biomarkers, more commonly referred to as radiomics 'features", provide readily available, cost-effective, non-invasive tools for screening, detecting tumors and serial monitoring of patients, including assessments of response to therapy and identification of therapeutic complications. Radiomics offers the potential to analyse whole tumors in 3D, as well as sub-regions or 'habitats' within tumors. Combining quantitative information from imaging with pathology, demographic details and other biomarkers will pave the way for personalised treatment selection and monitoring in prostate cancer. The aim of this review is to consider if MRI-based radiomics can bridge the gap between population-based management and personalised management of prostate cancer.
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Affiliation(s)
- Michelle Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland.
| | - Sarah Osman
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Lisburn Road, Belfast, BT9 7AE, United Kingdom
| | - Suneil Jain
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Lisburn Road, Belfast, BT9 7AE, United Kingdom
| | - Laure Marignol
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland
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Impact of radiomics on prostate cancer detection: a systematic review of clinical applications. Curr Opin Urol 2020; 30:754-781. [DOI: 10.1097/mou.0000000000000822] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Xu L, Zhang G, Zhao L, Mao L, Li X, Yan W, Xiao Y, Lei J, Sun H, Jin Z. Radiomics Based on Multiparametric Magnetic Resonance Imaging to Predict Extraprostatic Extension of Prostate Cancer. Front Oncol 2020; 10:940. [PMID: 32612953 PMCID: PMC7308458 DOI: 10.3389/fonc.2020.00940] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 05/13/2020] [Indexed: 01/30/2023] Open
Abstract
Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa). Methods: Ninety-five pathology-confirmed PCa patients with 115 lesions (49 positive and 66 negative) were retrospectively enrolled. A 3.0T MR scanner was used to perform T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Radiomics features extracted from T2WI, DWI, apparent diffusion coefficient (ADC), and DCE were used to build a radiomics model. Patients' clinical and pathological variables were also obtained to build a clinical model. The radiomics model and clinical model were further integrated to build a combined nomogram. All lesions were randomly divided into the training group (82 lesions) and the validation group (33 lesions). A least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to build the radiomics model. The diagnostic performance of different models was assessed by calculating the area under the curve (AUC) and compared using the Delong test. The calibration curve and decision curve analyses were used to assess the calibration and clinical usefulness of the radiomics model. Results: The AUC values for the radiomics model in the training and validation group were 0.919 and 0.865, respectively, with a good calibration performance. The decision curve analysis confirmed the clinical utility of the radiomics model. The accuracy, sensitivity, and specificity were 81.8, 71.4, and 89.5% in the validation group. In the validation group, the radiomics model outperformed the clinical model (AUC = 0.658, P = 0.020), and was comparable with the combined nomogram (AUC = 0.857, P = 0.644). Conclusion: The radiomics model based on mpMRI could different EPE and non-EPE lesions with satisfactory diagnostic performance, and this model might assist in predicting EPE before prostatectomy.
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Affiliation(s)
- Lili Xu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Gumuyang Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Lun Zhao
- Deepwise AI Lab, Deepwise Inc., Beijing, China
| | - Li Mao
- Deepwise AI Lab, Deepwise Inc., Beijing, China
| | - Xiuli Li
- Deepwise AI Lab, Deepwise Inc., Beijing, China
| | - Weigang Yan
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yu Xiao
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Lei
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Sun
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Pan R, Yang X, Shu Z, Gu Y, Weng L, Jia Y, Feng J. Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:1207-1218. [PMID: 32925162 DOI: 10.3233/xst-200695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
OBJECTIVE To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer. METHODS Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9-10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer. RESULTS Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9-10 (P < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9-10). The entropy and energy values correlate with GS (r = 0.767, r = -0.692, P < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8. CONCLUSION Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency.
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Affiliation(s)
- Ruigen Pan
- Department of Radiology, Zhuji affiliated hospital of Shaoxing University, Shaoxing, Zhejiang, China
| | - Xueli Yang
- Department of Radiology, Zhuji Fourth People's hospital, Zhuji, Zhejiang, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yifeng Gu
- Department of Radiology, Zhuji affiliated hospital of Shaoxing University, Shaoxing, Zhejiang, China
| | - Lihua Weng
- Department of Radiology, Zhuji affiliated hospital of Shaoxing University, Shaoxing, Zhejiang, China
| | - Yuezhu Jia
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jianju Feng
- Department of Radiology, Zhuji affiliated hospital of Shaoxing University, Shaoxing, Zhejiang, China
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