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Guerra A, Wang H, Orton MR, Konidari M, Papanikolaou NK, Koh DM, Donato H, Alves FC. Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review. Insights Imaging 2024; 15:217. [PMID: 39186182 PMCID: PMC11347513 DOI: 10.1186/s13244-024-01776-8] [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: 01/08/2024] [Accepted: 07/10/2024] [Indexed: 08/27/2024] Open
Abstract
The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE. CRITICAL RELEVANT STATEMENT The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients. PROTOCOL OF SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 . KEY POINTS Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.
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Affiliation(s)
- Adalgisa Guerra
- Department of Radiology, Hospital da Luz Lisbon, Lisboa, Portugal.
| | - Helen Wang
- Royal Surrey County Hospital HSH Foundation Trust. Royal Marsden Hospital NHS Foundation Trust, London, England
| | - Matthew R Orton
- Royal Marsden Hospital NHS Foundation Trust, London, England
| | | | | | - Dow Mu Koh
- Royal Marsden Hospital NHS Foundation Trust, London, England
| | - Helena Donato
- Documentation and Scientific Information Service, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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Chen X, Wang C, Chen Y, Qian C, Huang R, Bao J, Lin Y, Hou J, Huang Y, Wei X. Clinical risk prediction model and external validation of positive surgical margin in laparoscopic radical prostatectomy based on MRI lesion location. Clin Transl Oncol 2024; 26:1998-2005. [PMID: 38472559 DOI: 10.1007/s12094-024-03424-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/24/2024] [Indexed: 03/14/2024]
Abstract
OBJECTIVE To clarify the composition of lesions in different magnetic resonance imaging (MRI) partitions of positive surgical margins (PSM) after laparoscopic radical prostatectomy, explore the influence of lesion location on PSM, and construct a clinical prediction model to predict the risk of PSM. MATERIALS AND METHODS This retrospective cohort study included 309 patients who underwent laparoscopic radical prostatectomy from 2018 to 2021 in our center was performed. 129 patients who met the same criteria from January to September 2022 were external validation cohorts. RESULTS The incidence of PSM in transition zone (TZ) lesions was higher than that in peripheral zone (PZ) lesions. The incidence of PSM in the middle PZ was lower than that in other regions. Prostate specific antigen (PSA), clinical T-stage, the number of positive cores, international society of urological pathology (ISUP) grade (biopsy), MRI lesion location, extracapsular extension, seminal vesicle invasion (SVI), pseudo-capsule invasion (PCI), long diameter of lesions, lesion volume, lesion volume ratio, PSA density were related to PSM. MRI lesion location and PCI were independent risk factors for PSM. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a clinical prediction model for PSM, including five variables: the number of positive cores, SVI, MRI lesion location, long diameter of lesions, and PSA. CONCLUSION The positive rate of surgical margin in middle PZ was significantly lower than that in other regions, and MRI lesion location was an independent risk factor for PSM.
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Affiliation(s)
- Xin Chen
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
- Department of Urology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, People's Republic of China
| | - Chaozhong Wang
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Yongchang Chen
- Department of Urology, Changshu No. 2 People's Hospital, Suzhou, 215006, People's Republic of China
| | - Chengbo Qian
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Renpeng Huang
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People's Republic of China
| | - Jie Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People's Republic of China
| | - Yuxin Lin
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Jianquan Hou
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China.
- Department of Urology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, People's Republic of China.
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China.
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China.
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Ren L, Chen Y, Liu Z, Huang G, Wang W, Yang X, Bai B, Guo Y, Ling J, Mao X. Integration of PSAd and multiparametric MRI to forecast biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml. Front Oncol 2024; 14:1413953. [PMID: 39026982 PMCID: PMC11254766 DOI: 10.3389/fonc.2024.1413953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Introduction This study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy. Methods This retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models. Results The CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed. Conclusion The prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.
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Affiliation(s)
- Lei Ren
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Yanling Chen
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Zixiong Liu
- Department of Urology, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, China
| | - Guankai Huang
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Weifeng Wang
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
- Department of Urology, Hui Ya Hospital of The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Huizhou, China
| | - Xu Yang
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Baohua Bai
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
| | - Jian Ling
- Department of Radiology, The Eastern Hospital of the First Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Guangzhou, China
| | - Xiaopeng Mao
- Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China
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Ponsiglione A, Gambardella M, Stanzione A, Green R, Cantoni V, Nappi C, Crocetto F, Cuocolo R, Cuocolo A, Imbriaco M. Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis. Eur Radiol 2024; 34:3981-3991. [PMID: 37955670 PMCID: PMC11166859 DOI: 10.1007/s00330-023-10427-3] [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: 05/02/2023] [Revised: 09/10/2023] [Accepted: 09/27/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction. MATERIALS AND METHODS Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity. RESULTS Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76. CONCLUSION MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed. CLINICAL RELEVANCE STATEMENT Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements. KEY POINTS • MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.
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Affiliation(s)
- Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | | | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy.
| | - Roberta Green
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Valeria Cantoni
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Carmela Nappi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Felice Crocetto
- Department of Neurosciences, Human Reproduction and Odontostomatology, University of Naples Federico II, Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy
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Wen J, Liu W, Zhang Y, Shen X. MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:702-711. [PMID: 38520649 DOI: 10.1007/s11547-024-01810-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 03/13/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE We to systematically evaluate the diagnostic performance of MRI radiomics in detecting extracapsular extension (EPE) of prostate cancer (PCa). METHODS A literature search of online databases of PubMed, EMBASE, Cochrane Library, Web of Science, and Google Scholar online scientific publication databases was performed to identify studies published up to July 2023. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic (HSROC) model. This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, the quality of included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2) and the radiomics quality score (RQS). Meta-regression and subgroup analyses were performed to explore the impact of varying clinical settings. RESULTS A total of ten studies met the inclusion criteria. The pooled sensitivity and specificity were 0.77 (95% CI 0.68-0.84, I2 = 83.5%) and 0.75 (95% CI 0.67-0.82, I2 = 83.5%), respectively, with an area under the HSROC curve of 0.88 (95% CI 0.85-0.91). Study quality was not high while assessing with the RQS. Substantial heterogeneity was observed between studies; however, meta-regression analysis did not reveal any significant contributing factors. CONCLUSIONS MRI radiomics demonstrated moderate sensitivity and specificity, offering similar diagnostic performance with previous risk stratifications and models that primarily based on radiologists' subjective experience. However, all studies included were retrospective, thus the performance of radiomics needs to validate in prospective, multicenter studies.
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Affiliation(s)
- Jing Wen
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China.
| | - Wei Liu
- Department of Radiology, Yancheng Tinghu District People's Hospital, Yancheng, China
| | - Yilan Zhang
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
| | - Xiaocui Shen
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
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Ma J, Chen K, Li S, Zhu L, Yu Y, Li J, Ma J, Ouyang J, Wu Z, Tan Y, He Z, Liu H, Pan Z, Li H, Liu Q, Song E. MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study. Eur Radiol 2024; 34:1774-1789. [PMID: 37658888 DOI: 10.1007/s00330-023-10144-x] [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: 04/07/2022] [Revised: 05/18/2023] [Accepted: 07/08/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status. METHODS We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status. RESULTS The AUCs of the radiomic-based model were 0.78 (0.66-0.90), 0.88 (0.79-0.96), and 0.76 (0.68-0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0-10% and 27.3-87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area. CONCLUSIONS This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status. CLINICAL RELEVANCE STATEMENT This study can assist the pre-operative planning of BCS. Further research on the tumor immune microenvironment of different resection margin states is expected to develop new margin evaluation indicators and decipher the internal mechanism. KEY POINTS • The MRI-based radiomic prediction model (CSS model) incorporating features extracted from multiple sequences and segments could estimate the margin positivity risk of breast-conserving surgery. • The radiomic score of the CSS model allows risk stratification of patients undergoing breast-conserving surgery, which could assist in surgical planning. • With the help of MRI-based radiomics to estimate the components of the immune microenvironment, for the first time, it is found that the margin status of breast-conserving surgery is associated with the infiltration of immune cells in the microenvironment and the EMT status of breast tumor cells.
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Affiliation(s)
- Jiafan Ma
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Shunrong Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Liling Zhu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Yunfang Yu
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Jingwu Li
- Department of Breast Surgery, Tangshan People's Hospital, Tangshan, 063001, Hebei, China
| | - Jie Ma
- Department of Breast Surgery, Tangshan People's Hospital, Tangshan, 063001, Hebei, China
| | - Jie Ouyang
- Department of Breast Surgery, Tungwah Hospital, Sun Yat-sen University, Dongguan, 523413, China
| | - Zhuo Wu
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Yujie Tan
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Zifan He
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Haiqing Liu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Zhilong Pan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China
| | - Haojiang Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China.
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, Guangdong, China.
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Guerra A, Orton MR, Wang H, Konidari M, Maes K, Papanikolaou NK, Koh DM. Clinical application of machine learning models in patients with prostate cancer before prostatectomy. Cancer Imaging 2024; 24:24. [PMID: 38331808 PMCID: PMC10854130 DOI: 10.1186/s40644-024-00666-y] [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: 05/13/2023] [Accepted: 01/21/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models. METHODS This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models' net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC). RESULTS In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only. CONCLUSIONS The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.
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Affiliation(s)
- Adalgisa Guerra
- Department of Radiology, Hospital da Luz Lisbon, Rua Fernando Curado Ribeiro, 2, 7º esq, 1495-094, Algés, Lisboa, Portugal.
| | - Matthew R Orton
- Royal Marsden Hospital NHS Foundation Trust, London, England
| | - Helen Wang
- Royal Surrey County Hospital NSH Foundation Trust, Royal Marsden Hospital NHS Foundation Trust, London, England
| | | | - Kris Maes
- Department of Urology, Hospital da Luz Lisbon, Lisbon, Portugal
| | | | - Dow Mu Koh
- Royal Marsden Hospital NHS Foundation Trust, London, England
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Pan K, Yao F, Hong W, Xiao J, Bian S, Zhu D, Yuan Y, Zhang Y, Zhuang Y, Yang Y. Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction. Br J Radiol 2024; 97:408-414. [PMID: 38308032 DOI: 10.1093/bjr/tqad038] [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: 08/12/2023] [Revised: 11/08/2023] [Accepted: 11/20/2023] [Indexed: 02/04/2024] Open
Abstract
OBJECTIVES To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE. METHODS We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix. RESULTS AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05). CONCLUSION The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions. ADVANCES IN KNOWLEDGE This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.
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Affiliation(s)
- Kehua Pan
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Fei Yao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Weifeng Hong
- Department of Radiology, The People's Hospital of Yuhuan, Taizhou 318000, China
| | - Juan Xiao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Shuying Bian
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Dongqin Zhu
- Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yaping Yuan
- The First Clinical Medical College, Wenzhou Medical University, Wenzhou 325000, China
| | - Yayun Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yuandi Zhuang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yunjun Yang
- Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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9
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Feng Y, Feng Z, Wang L, Lv W, Liu Z, Min X, Li J, Zhang J. Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics. Front Med (Lausanne) 2023; 10:1279622. [PMID: 38188340 PMCID: PMC10768048 DOI: 10.3389/fmed.2023.1279622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024] Open
Abstract
Objective Accurate identification of testicular tumors through better lesion characterization can optimize the radical surgical procedures. Here, we compared the performance of different machine learning approaches for discriminating benign testicular lesions from malignant ones, using a radiomics score derived from magnetic resonance imaging (MRI). Methods One hundred fifteen lesions from 108 patients who underwent MRI between February 2014 and July 2022 were enrolled in this study. Based on regions-of-interest, radiomics features extraction can be realized through PyRadiomics. For measuring feature reproducibility, we considered both intraclass and interclass correlation coefficients. We calculated the correlation between each feature and the predicted target, removing redundant features. In our radiomics-based analysis, we trained classifiers on 70% of the lesions and compared different models, including linear discrimination, gradient boosting, and decision trees. We applied each classification algorithm to the training set using different random seeds, repeating this process 10 times and recording performance. The highest-performing model was then tested on the remaining 30% of the lesions. We used widely accepted metrics, such as the area under the curve (AUC), to evaluate model performance. Results We acquired 1,781 radiomic features from the T2-weighted maps of each lesion. Subsequently, we constructed classification models using the top 10 most significant features. The 10 machine-learning algorithms we utilized were capable of diagnosing testicular lesions. Of these, the XGBoost classification emerged as the most superior, achieving the highest AUC value of 0.905 (95% confidence interval: 0.886-0.925) on the testing set and outstripping the other models that typically scored AUC values between 0.697-0.898. Conclusion Preoperative MRI radiomics offers potential for distinguishing between benign and malignant testicular lesions. An ensemble model like the boosting algorithm embodied by XGBoost may outperform other models.
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Affiliation(s)
- Yanhui Feng
- School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China
| | - Zhaoyan Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liang Wang
- Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhi Lv
- Britton Chance Center and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Zhiyong Liu
- School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangde Min
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jin Li
- Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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van den Berg I, Soeterik TFW, van der Hoeven EJRJ, Claassen B, Brink WM, Baas DJH, Sedelaar JPM, Heine L, Tol J, van der Voort van Zyp JRN, van den Berg CAT, van den Bergh RCN, van Basten JPA, van Melick HHE. The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer. Cancers (Basel) 2023; 15:5452. [PMID: 38001712 PMCID: PMC10670855 DOI: 10.3390/cancers15225452] [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: 10/21/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.
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Affiliation(s)
- Ingeborg van den Berg
- Department of Urology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands
- Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Magnetic Detection and Imaging Group, Technical Medical Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Timo F. W. Soeterik
- Department of Urology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands
- Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | | | - Bart Claassen
- Department of Radiology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands
| | - Wyger M. Brink
- Magnetic Detection and Imaging Group, Technical Medical Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Diederik J. H. Baas
- Department of Urology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands
| | - J. P. Michiel Sedelaar
- Department of Urology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Lizette Heine
- Quantib B.V., RadNet’s AI Division, 3012 KM Rotterdam, The Netherlands
| | - Jim Tol
- Quantib B.V., RadNet’s AI Division, 3012 KM Rotterdam, The Netherlands
| | | | - Cornelis A. T. van den Berg
- Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | | | - Jean-Paul A. van Basten
- Department of Urology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands
- Department of Urology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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Lin Z, Gu W, Guo Q, Xiao M, Li R, Deng L, Li Y, Cui Y, Li H, Qiang J. Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer. Br J Radiol 2023; 96:20221063. [PMID: 37660398 PMCID: PMC10607390 DOI: 10.1259/bjr.20221063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. METHODS The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. RESULTS Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. CONCLUSIONS The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. ADVANCES IN KNOWLEDGE This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.
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Affiliation(s)
| | - Weiyong Gu
- Department of Pathology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | | | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | | | - Lin Deng
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | | | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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Valeri A, Nguyen TA. Research on texture images and radiomics in urology: a review of urological MR imaging applications. Curr Opin Urol 2023; 33:428-436. [PMID: 37727910 DOI: 10.1097/mou.0000000000001131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
PURPOSE OF REVIEW Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives. RECENT FINDINGS Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization. SUMMARY Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.
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Affiliation(s)
- Antoine Valeri
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
- CeRePP, Paris, France
| | - Truong An Nguyen
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
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Wang H, Qiu J, Xie J, Lu W, Pan Y, Ma J, Jia M. Radiomics‑Clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients. J Cancer Res Clin Oncol 2023; 149:13353-13361. [PMID: 37491635 DOI: 10.1007/s00432-023-05162-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 07/09/2023] [Indexed: 07/27/2023]
Abstract
BACKGROUND To establish a radiomics-clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients. METHODS We retrospectively analyzed 256 patients (122 with bone metastasis and 134 with benign bone disease) and randomized them in the ratio of 6:2:2 into training, test and validation sets. All patients underwent 99mTc-labeled methylene diphosphonate (99mTc-MDP) SPECT/CT. We manually outlined the volumes of interest (VOIs) of lesions using ITK-SNAP from SPECT and CT images. In the training set, radiomics features were extracted using PyRadiomics and selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we established three radiomics models (CT, SPECT and SPECT-CT models) using support vector machine (SVM). In addition, a radiomics-clinical model was constructed using multivariable logistic regression analysis. The four models' performance was assessed using the area under the receiver operating characteristic curve (AUC). Using DeLong test to make comparisons between the ROC (receiver operating characteristic) curves of different models. The clinical utility of the models was evaluated using decision curve analysis (DCA). RESULTS The radiomics-clinical displayed excellent performance, and its AUC was 0.941 and 0.879 in the training and test sets. The DCA of radiomics-clinical model showed the highest clinical utility. CONCLUSIONS The radiomics-clinical nomogram for identifying bone metastasis and benign bone disease in tumor patients was suitable to assist in clinical decision.
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Affiliation(s)
- Huili Wang
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250012, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China
| | - Jindong Xie
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250012, China
| | - Weizhao Lu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China
| | - Yuteng Pan
- Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250000, China
| | - Junchi Ma
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China.
| | - Mingsheng Jia
- Department of Nuclear Medicine, The Second Affiliated Hospital of Shandong First Medical University, Taishan Street, No.706, Taian, 271000, China.
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Xu L, Zhang G, Zhang D, Zhang J, Zhang X, Bai X, Chen L, Peng Q, Xiao Y, Wang H, Jin Z, Sun H. An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy. Insights Imaging 2023; 14:178. [PMID: 37872408 PMCID: PMC10593712 DOI: 10.1186/s13244-023-01516-4] [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: 06/05/2023] [Accepted: 09/03/2023] [Indexed: 10/25/2023] Open
Abstract
OBJECTIVE To construct a simplified grading system based on MRI features to predict positive surgical margin (PSM) after radical prostatectomy (RP). METHODS Patients who had undergone prostate MRI followed by RP between January 2017 and January 2021 were retrospectively enrolled as the derivation group, and those between February 2021 and November 2022 were enrolled as the validation group. One radiologist evaluated tumor-related MRI features, including the capsule contact length (CCL) of lesions, frank extraprostatic extension (EPE), apex abutting, etc. Binary logistic regression and decision tree analysis were used to select risk features for PSM. The area under the curve (AUC), sensitivity, and specificity of different systems were calculated. The interreader agreement of the scoring systems was evaluated using the kappa statistic. RESULTS There were 29.8% (42/141) and 36.4% (32/88) of patients who had PSM in the derivation and validation cohorts, respectively. The first grading system was proposed (mrPSM1) using two imaging features, namely, CCL ≥ 20 mm and apex abutting, and then updated by adding frank EPE (mrPSM2). In the derivation group, the AUC was 0.705 for mrPSM1 and 0.713 for mrPSM2. In the validation group, our grading systems showed comparable AUC with Park et al.'s model (0.672-0.686 vs. 0.646, p > 0.05) and significantly higher specificity (0.732-0.750 vs. 0.411, p < 0.001). The kappa value was 0.764 for mrPSM1 and 0.776 for mrPSM2. Decision curve analysis showed a higher net benefit for mrPSM2. CONCLUSION The proposed grading systems based on MRI could benefit the risk stratification of PSM and are easily interpretable. CRITICAL RELEVANCE STATEMENT The proposed mrPSM grading systems for preoperative prediction of surgical margin status after radical prostatectomy are simplified compared to a previous model and show high specificity for identifying the risk of positive surgical margin, which might benefit the management of prostate cancer. KEY POINTS • CCL ≥ 20 mm, apex abutting, and EPE were important MRI features for PSM. • Our proposed MRI-based grading systems showed the possibility to predict PSM with high specificity. • The MRI-based grading systems might facilitate a structured risk evaluation of PSM.
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Affiliation(s)
- 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, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China
- National Center for Quality Control of Radiology, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Yu Xiao
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Hao Wang
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
- National Center for Quality Control of Radiology, No.1 Shuaifuyuan, 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, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
- National Center for Quality Control of Radiology, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
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Wu Y, Pan W, Wang L, Pan W, Zhang H, Jin S, Wu X, Liu A, Xin E, Ji W. A Comprehensive Model Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging Can Better Predict the Preoperative Histological Grade of Breast Cancer Than a Radiomics Model. BREAST CANCER (DOVE MEDICAL PRESS) 2023; 15:709-720. [PMID: 37873521 PMCID: PMC10590555 DOI: 10.2147/bctt.s425996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/30/2023] [Indexed: 10/25/2023]
Abstract
Background Histological grade is an important prognostic factor for patients with breast cancer and can affect clinical decision-making. From a clinical perspective, developing an efficient and non-invasive method for evaluating histological grading is desirable, facilitating improved clinical decision-making by physicians. This study aimed to develop an integrated model based on radiomics and clinical imaging features for preoperative prediction of histological grade invasive breast cancer. Methods In this retrospective study, we recruited 211 patients with invasive breast cancer and randomly assigned them to either a training group (n=147) or a validation group (n=64) with a 7:3 ratio. Patients were classified as having low-grade tumors, which included grade I and II tumors, or high-grade tumors, which included grade III tumors. Three models were constructed based on basic clinical features, radiomics features, and the sum of the two. To assess diagnostic performance of the radiomics models, we employed measures such as receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity, and the predictive performance of the three models was compared using the DeLong test and net reclassification improvement (NRI). Results The area under the curve (AUC) of the clinical model, radiomics model, and comprehensive model was 0.682, 0.833, and 0.882 in the training set and 0.741, 0.751, and 0.836 in the validation set, respectively. NRI analysis confirmed that the combined model was better than the other two models in predicting the histological grade of breast cancer (NRI=21.4% in the testing cohort). Conclusion Compared with the other models, the comprehensive model based on the combination of basic clinical features and radiomics features exhibits more significant potential for predicting histological grade and can better assist clinicians in optimal decision-making.
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Affiliation(s)
- Yitian Wu
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, 312000, People’s Republic of China
| | - Weixing Pan
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, 317000, People’s Republic of China
| | - Lingxia Wang
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, 317000, People’s Republic of China
| | - Wenting Pan
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, 317000, People’s Republic of China
| | - Huangqi Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, 317000, People’s Republic of China
| | - Shengze Jin
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, 312000, People’s Republic of China
| | - Xiuli Wu
- Department of Nuclear Medicine, Taizhou Hospital of Zhejiang Province, Linhai, Zhejiang, 317000, People’s Republic of China
| | - Aie Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, People’s Republic of China
| | - Enhui Xin
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, People’s Republic of China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, Zhejiang, 312000, People’s Republic of China
- Key Laboratory of Evidence-Based Radiology of Taizhou, Linhai, Zhejiang, 317000, People’s Republic of China
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Volpe F, Nappi C, Piscopo L, Zampella E, Mainolfi CG, Ponsiglione A, Imbriaco M, Cuocolo A, Klain M. Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives. Cancers (Basel) 2023; 15:4746. [PMID: 37835440 PMCID: PMC10571937 DOI: 10.3390/cancers15194746] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Prostate cancer is the most frequent epithelial neoplasia after skin cancer in men starting from 50 years and prostate-specific antigen (PSA) dosage can be used as an early screening tool. Prostate cancer imaging includes several radiological modalities, ranging from ultrasonography, computed tomography (CT), and magnetic resonance to nuclear medicine hybrid techniques such as single-photon emission computed tomography (SPECT)/CT and positron emission tomography (PET)/CT. Innovation in radiopharmaceutical compounds has introduced specific tracers with diagnostic and therapeutic indications, opening the horizons to targeted and very effective clinical care for patients with prostate cancer. The aim of the present review is to illustrate the current knowledge and future perspectives of nuclear medicine, including stand-alone diagnostic techniques and theragnostic approaches, in the clinical management of patients with prostate cancer from initial staging to advanced disease.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Michele Klain
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80138 Naples, Italy; (F.V.); (C.N.); (L.P.); (E.Z.); (C.G.M.); (A.P.); (M.I.); (A.C.)
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Duenweg SR, Bobholz SA, Barrett MJ, Lowman AK, Winiarz A, Nath B, Stebbins M, Bukowy J, Iczkowski KA, Jacobsohn KM, Vincent-Sheldon S, LaViolette PS. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers (Basel) 2023; 15:4437. [PMID: 37760407 PMCID: PMC10526331 DOI: 10.3390/cancers15184437] [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: 06/30/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.
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Affiliation(s)
- Savannah R. Duenweg
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Michael J. Barrett
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Margaret Stebbins
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, 1025 N Broadway, Milwaukee, WI 53202, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Kenneth M. Jacobsohn
- Department of Urology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Stephanie Vincent-Sheldon
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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Zhu M, Gao J, Han F, Yin L, Zhang L, Yang Y, Zhang J. Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis. Insights Imaging 2023; 14:140. [PMID: 37606802 PMCID: PMC10444717 DOI: 10.1186/s13244-023-01486-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/19/2023] [Indexed: 08/23/2023] Open
Abstract
PURPOSE In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs. MATERIALS AND METHODS The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis. RESULTS Forty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively. CONCLUSION Preoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients. CRITICAL RELEVANCE STATEMENT This meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients. KEY POINTS • MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.
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Affiliation(s)
- MeiLin Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - JiaHao Gao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Fang Han
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - LongLin Yin
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - LuShun Zhang
- Department of Pathology and Pathophysiology, Chengdu Medical College, Development and Regeneration Key Laboratory of Sichuan Province, Chengdu, 610500, China
| | - Yong Yang
- School of Big Health & Intelligent Engineering, Chengdu Medical College, Chengdu, 610500, China.
| | - JiaWen Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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Chen X, Li W, Yang J, Huang C, Zhou C, Chen Y, Lin Y, Hou J, Huang Y, Wei X. Extracapsular extension of transitional zone prostate cancer miss-detected by multiparametric magnetic resonance imaging. J Cancer Res Clin Oncol 2023; 149:6943-6952. [PMID: 36847840 DOI: 10.1007/s00432-023-04573-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/04/2023] [Indexed: 03/01/2023]
Abstract
OBJECTIVES To demonstrate the importance of extracapsular extension (ECE) of transitional zone (TZ) prostate cancer (PCa), examine the causes of its missed detection by Mp-MRI, and develop a new predictive model by integrating multi-level clinical variables. MATERIALS AND METHODS This retrospective study included 304 patients who underwent laparoscopic radical prostatectomy after 12 + X needle transperineal transrectal ultrasound (TRUS)-MRI-guided targeted prostate biopsy from 2018 to 2021 in our center was performed. RESULTS In this study, the incidence rates of ECE were similar in patients with MRI lesions in the peripheral zone (PZ) and TZ (P = 0.66). However, the missed detection rate was higher in patients with TZ lesions than in those with PZ lesions (P < 0.05). These missed detections result in a higher positive surgical margin rate (P < 0.05). In patients with TZ lesions, detected MP-MRI ECE may have grey areas: the longest diameters of the MRI lesions were 16.5-23.5 mm; MRI lesion volumes were 0.63-2.51 ml; MRI lesion volume ratios were 2.75-8.86%; PSA were 13.85-23.05 ng/ml. LASSO regression was used to construct a clinical prediction model for predicting the risk of ECE in TZ lesions from the perspective of MRI and clinical features, including four variables: the longest diameter of MRI lesions, TZ pseudocapsule invasion, ISUP grading of biopsy pathology, and number of positive biopsy needles. CONCLUSIONS Patients with MRI lesions in the TZ have the same incidence of ECE as those with lesions in the PZ, but a higher missed detection rate.
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Affiliation(s)
- Xin Chen
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Wei Li
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Jiajian Yang
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Chen Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Chenchao Zhou
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Yongchang Chen
- Department of Urology, Changshu No. 2 People's Hospital, Suzhou, 215006, People's Republic of China
| | - Yuxin Lin
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China
| | - Jianquan Hou
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China.
- Department of Urology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, People's Republic of China.
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China.
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215006, People's Republic of China.
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20
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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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Li C, Deng M, Zhong X, Ren J, Chen X, Chen J, Xiao F, Xu H. Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI. Front Oncol 2023; 13:1198899. [PMID: 37448515 PMCID: PMC10338012 DOI: 10.3389/fonc.2023.1198899] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
Introduction This study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia. Methods A total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison. Results The optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model's predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics. Discussion The constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies.
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Affiliation(s)
- Chunyu Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Ming Deng
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoli Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jinxia Ren
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaohui Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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22
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Huang C, Rui R, Feng N, He Q, Gong Y, Li X, He S, Zhou L. Multiparametric Magnetic Resonance Imaging-based Prostate Specific Antigen Density and PI-RADSv2 score help identify Apical Prostate Cancer. J Cancer 2023; 14:1371-1380. [PMID: 37283788 PMCID: PMC10240671 DOI: 10.7150/jca.84278] [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: 03/14/2023] [Accepted: 04/27/2023] [Indexed: 06/08/2023] Open
Abstract
Objective: To investigate the potential roles of preoperative multiparametric magnetic resonance imaging (mpMRI) in identifying aggressive apical prostate cancer (APCa), thereby helping to facilitate patient counseling and surgical planning. Patients and Methods: We performed a retrospective analysis of 662 patients who underwent radical prostatectomy (RP) between January 2010 to October 2019. All patients underwent a preoperative biopsy and mpMRI of the prostate. APCa was defined as any malignant lesions in the prostatic apex. Clinical, pathological and mpMRI variables were retrieved. Univariate, multivariate, and receiver operating characteristic (ROC) analyses were performed. Results: A total of 214 (32.3%) patients had APCa. Patients presenting APCa were more likely to harbor adverse clinicopathological features (all p < 0.05). On univariable analysis, serum prostate-specific antigen (PSA) (p < 0.001), mpMRI-based PSA density (PSAD) (p < 0.001), Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) score (p < 0.001), number of positive cores (p < 0.001), percentage of positive cores (p < 0.001), max core involvement (p < 0.001) and biopsy GG (p = 0.001) were significant predictors of APCa. On multivariable analysis, mpMRI-based PSAD ≥ 0.27 ng/ml/cm3 (odds ratio [OR]: 2.251, p = 0.003), PI-RADSv2 score > 4 (OR: 1.611, p = 0.023) and percentage of positive cores (OR: 2.333, p = 0.041) were independently predictive of APCa during RP. The AUC values of mpMRI-based PSAD and PI-RADSv2 score were 0.646 (95% Confidence Intervals [CI]: 0.608-0.682) and 0.612 (95% CI: 0.568-0.656), respectively. Conclusion: Preoperative mpMRI-based PSAD and PI-RADSv2 score help identify the presence of APCa and may be useful for surgical decision-making during RP.
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Affiliation(s)
- Cong Huang
- Department of Urology, Peking University First Hospital, Beijing 100034, China
- Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing 100034, China
- Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, China
| | - Rui Rui
- Department of Urology, Peking University First Hospital, Beijing 100034, China
- Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing 100034, China
- Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, China
| | - Ninghan Feng
- Department of Urology, Affiliated Wuxi No. 2 Hospital of Nanjing Medical University, Wuxi 214002, China
- Wuxi Medical College, Jiangnan University, Wuxi 214002, China
| | - Qun He
- Department of Urology, Peking University First Hospital, Beijing 100034, China
- Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing 100034, China
- Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, China
| | - Yanqing Gong
- Department of Urology, Peking University First Hospital, Beijing 100034, China
- Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing 100034, China
- Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, China
| | - Xuesong Li
- Department of Urology, Peking University First Hospital, Beijing 100034, China
- Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing 100034, China
- Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, China
| | - Shiming He
- Department of Urology, Peking University First Hospital, Beijing 100034, China
- Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing 100034, China
- Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, China
| | - Liqun Zhou
- Department of Urology, Peking University First Hospital, Beijing 100034, China
- Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing 100034, China
- Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center, Beijing 100034, China
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He J, Che B, Li P, Li W, Huang T, Chen P, Liu M, Li G, Zhong S, Tang K. Ki67 and the apparent diffusion coefficient in postoperative prostate cancer with endocrine therapy. Front Surg 2023; 10:1140883. [PMID: 37091270 PMCID: PMC10113680 DOI: 10.3389/fsurg.2023.1140883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
BackgroundProstate-specific antigen (PSA) is a critical part of prostate cancer (PCa) screening, diagnosis, staging, and prognosis. However, elevated PSA levels can also be caused by several external factors. To improve the specificity and sensitivity of PSA in clinical practice, we explored whether markers or parameters may be used as prognostic targets for PCa by long-term follow-up.MethodsA total of 121 PCa patients who underwent laparoscopic radical prostatectomy (LRP) were included in our study, all of whom underwent imaging and preoperative pathological diagnosis. Endocrine therapy has long been applied to treat postoperative patients. The prognosis of enrolled patients was followed, and statistics were collected. Spearman's correlation analysis was applied to examine the relationship and clinical parameters. Kaplan–Meier analysis was used to process the clinical variables of PCa patients. Cox proportional hazards regression analysis was applied to examine univariate and multivariate variables.ResultsThe Gleason score (GS), PSA, clinical stage, nerve infiltration, organ confinement, Ki67 and apparent diffusion coefficient (ADC) were significantly associated with prognosis (all P < 0.05). The GS, PSA, clinical stage, organ confined, Ki67, nerve infiltration and ADC were included in the multivariate analysis (all P < 0.05). Ultimately, Ki67 and the ADC were found to provide meaningful predictive information (both P < 0.05).ConclusionsKi67 and the ADC may be clinically and analytically valid prognostic biomarkers and imaging parameters in PCa. They may be useful for predicting the prognosis and risk of PCa patients undergoing postoperative routine endocrine therapy.
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Affiliation(s)
- Jun He
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Bangwei Che
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Po Li
- Department of Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Wei Li
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Tao Huang
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Peng Chen
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Miao Liu
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Guangyu Li
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Siwen Zhong
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Kaifa Tang
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Department of Urology and Andrology, The First Affiliated Hospital, Guizhou University of Traditional Chinese Medicine, Guiyang, China
- Correspondence: Kaifa Tang
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Huynh LM, Hwang Y, Taylor O, Baine MJ. The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature. Diagnostics (Basel) 2023; 13:diagnostics13061128. [PMID: 36980436 PMCID: PMC10047271 DOI: 10.3390/diagnostics13061128] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
The development of precise medical imaging has facilitated the establishment of radiomics, a computer-based method of quantitatively analyzing subvisual imaging characteristics. The present review summarizes the current literature on the use of diagnostic magnetic resonance imaging (MRI)-derived radiomics in prostate cancer (PCa) risk stratification. A stepwise literature search of publications from 2017 to 2022 was performed. Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. In predicting GS and adverse pathology, radiomic models differentiated well, with receiver operator characteristic area under the curve (ROC-AUC) values of 0.50–0.92 and 0.60–0.92, respectively. For studies predicting post-treatment recurrence or failure, ROC-AUC for radiomic models ranged from 0.73 to 0.99 in postsurgical and radiation cohorts. Finally, of the 33 studies, 7 (21.2%) included external validation. Overall, most investigations showed good to excellent prediction of GS and adverse pathology with MRI-derived radiomic features. Direct prediction of treatment outcomes, however, is an ongoing investigation. As these studies mature and reach potential for clinical integration, concerted effort to validate these radiomic models must be undertaken.
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Affiliation(s)
- Linda My Huynh
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
- Department of Urology, University of California, Orange, CA 92868, USA
| | - Yeagyeong Hwang
- Department of Urology, University of California, Orange, CA 92868, USA
| | - Olivia Taylor
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
| | - Michael J. Baine
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
- Correspondence:
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25
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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [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: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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Meng S, Chen L, Zhang Q, Wang N, Liu A. Multiparametric MRI-based nomograms in predicting positive surgical margins of prostate cancer after laparoscopic radical prostatectomy. Front Oncol 2022; 12:973285. [PMID: 36172161 PMCID: PMC9510973 DOI: 10.3389/fonc.2022.973285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/11/2022] [Indexed: 11/26/2022] Open
Abstract
Background Positive surgical margins (PSMs) are an independent risk factor of biochemical recurrence in patients with prostate cancer (PCa) after laparoscopic radical prostatectomy; however, limited MRI-based predictive tools are available. This study aimed to develop a novel nomogram combining clinical and multiparametric MRI (mpMRI) parameters to reduce PSMs by improving surgical planning. Methods One hundred and three patients with PCa (55 patients with negative surgical margins [NSMs] and 48 patients with PSMs) were included in this retrospective study. The following parameters were obtained using GE Functool post-processing software: diffusion-weighted imaging (DWI); intravoxel incoherent motion model (IVIM); and diffusion kurtosis imaging (DKI). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to analyze the data set to select the optimal MRI predictors. Preoperatively clinical parameters used to build a clinical nomogram (C-nomogram). Multivariable logistic regression analysis was used to build an MRI nomogram (M-nomogram) by introducing the MRI parameters. Based on the MRI and clinical parameters, build an MRI combined with clinical parameters nomogram (MC-nomogram). Comparisons with the M-nomogram and MC-nomogram were based on discrimination, calibration, and decision curve analysis (DCA). A 3-fold cross-validation method was used to assess the stability of the nomogram. Results There was no statistical difference in AUC between the C-nomogram (sensitivity=64%, specificity=65% and AUC=0.683), the M-nomogram (sensitivity=57%, specificity=88% and AUC=0.735) and the MC-nomogram (sensitivity= 64%, specificity=82% and AUC=0.756). The calibration curves of the three nomograms used to predict the risk of PSMs in patients with PCa showed good agreement. The net benefit of the MC-nomogram was higher than the others (range, 0.2-0.7). Conclusions The mpMRI-based nomogram can predict PSMs in PCa patients. Although its AUC (0.735) is not statistically different from that of the clinical-based nomogram AUC (0.683). However, mpMRI-based nomogram has higher specificity (88% VS. 63%), model stability, and clinical benefit than clinical-based nomogram. And the predictive ability of mpMRI plus clinical parameters for PSMs is further improved.
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Li C, Li W, Liu C, Zheng H, Cai J, Wang S. Artificial intelligence in multi-parametric magnetic resonance imaging: A review. Med Phys 2022; 49:e1024-e1054. [PMID: 35980348 DOI: 10.1002/mp.15936] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Peng Cheng Laboratory, Shenzhen, 518066, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
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More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review. J Pers Med 2022; 12:jpm12060983. [PMID: 35743766 PMCID: PMC9225075 DOI: 10.3390/jpm12060983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.
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Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040799. [PMID: 35453847 PMCID: PMC9027206 DOI: 10.3390/diagnostics12040799] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 02/04/2023] Open
Abstract
Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.
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Fan X, Xie N, Chen J, Li T, Cao R, Yu H, He M, Wang Z, Wang Y, Liu H, Wang H, Yin X. Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer. Front Oncol 2022; 12:839621. [PMID: 35198452 PMCID: PMC8859464 DOI: 10.3389/fonc.2022.839621] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 01/11/2022] [Indexed: 01/18/2023] Open
Abstract
Objectives This study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer. Methods A total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC. Results RF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong’s tests. Conclusions Radiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer.
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Affiliation(s)
- Xuhui Fan
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ni Xie
- Institution for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingwen Chen
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tiewen Li
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Cao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongwei Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Meijuan He
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zilin Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yihui Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Liu
- Department of Research and Development, Yizhun Medical AI Technology Co. Ltd., Beijing, China
| | - Han Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institution for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of Radiology, Jiading Branch of Shanghai General Hospital, Shanghai, China
| | - Xiaorui Yin
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics. Cancers (Basel) 2021; 13:cancers13236065. [PMID: 34885175 PMCID: PMC8657292 DOI: 10.3390/cancers13236065] [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: 11/02/2021] [Revised: 11/24/2021] [Accepted: 11/26/2021] [Indexed: 11/18/2022] Open
Abstract
Simple Summary The use of radiomics has been studied to predict Gleason Score from bi-parametric prostate MRI examinations. However, different combinations of type of input data (whole prostate gland/lesion features), sampling strategy, feature selection method and machine learning algorithm can be used. The impact of such choices was investigated and it was found that features extracted from the whole prostate gland were more stable to segmentation differences and produced better models (higher performance and less overfitting). This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion. Abstract Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.
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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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