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Antolin A, Roson N, Mast R, Arce J, Almodovar R, Cortada R, Maceda A, Escobar M, Trilla E, Morote J. The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review. Cancers (Basel) 2024; 16:2951. [PMID: 39272809 PMCID: PMC11393977 DOI: 10.3390/cancers16172951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 08/18/2024] [Accepted: 08/21/2024] [Indexed: 09/15/2024] Open
Abstract
Early detection of clinically significant prostate cancer (csPCa) has substantially improved with the latest PI-RADS versions. However, there is still an overdiagnosis of indolent lesions (iPCa), and radiomics has emerged as a potential solution. The aim of this systematic review is to evaluate the role of handcrafted and deep radiomics in differentiating lesions with csPCa from those with iPCa and benign lesions on prostate MRI assessed with PI-RADS v2 and/or 2.1. The literature search was conducted in PubMed, Cochrane, and Web of Science databases to select relevant studies. Quality assessment was carried out with Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), Radiomic Quality Score (RQS), and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. A total of 14 studies were deemed as relevant from 411 publications. The results highlighted a good performance of handcrafted and deep radiomics methods for csPCa detection, but without significant differences compared to radiologists (PI-RADS) in the few studies in which it was assessed. Moreover, heterogeneity and restrictions were found in the studies and quality analysis, which might induce bias. Future studies should tackle these problems to encourage clinical applicability. Prospective studies and comparison with radiologists (PI-RADS) are needed to better understand its potential.
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Affiliation(s)
- Andreu Antolin
- Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Nuria Roson
- Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Richard Mast
- Department of Radiology, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Javier Arce
- Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Ramon Almodovar
- Department of Radiology, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Roger Cortada
- Department of Radiology, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | | | - Manuel Escobar
- Department of Radiology, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Enrique Trilla
- Department of Surgery, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
- Department of Urology, Vall d'Hebron University Hospital, 08035 Barcelona, Spain
| | - Juan Morote
- Department of Surgery, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
- Department of Urology, Vall d'Hebron University Hospital, 08035 Barcelona, Spain
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2
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He H, Long Q, Li L, Fu Y, Wang X, Qin Y, Jiang M, Tan Z, Yi X, Chen BT. Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme. NMR IN BIOMEDICINE 2024:e5242. [PMID: 39164197 DOI: 10.1002/nbm.5242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/16/2024] [Accepted: 08/05/2024] [Indexed: 08/22/2024]
Abstract
This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled patients with histopathologically confirmed IEE and GBM from June 2016 to June 2021. Radiomics features were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) sequence images, and classification models were constructed using EL methods and logistic regression (LR). The efficiency of the models was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The combined EL model, based on clinical parameters and radiomic features from T1WI and T2WI images, demonstrated good discriminative ability, achieving an area under the receiver operating characteristics curve (AUC) of 0.96 (95% CI 0.94-0.98), a specificity of 0.84, an accuracy of 0.92, and a sensitivity of 0.95 in the training set, and an AUC of 0.89 (95% CI 0.83-0.94), a specificity of 0.83, an accuracy of 0.81, and a sensitivity of 0.74 in the validation set. The discriminative efficacy of the EL model was significantly higher than that of the LR model. Favorable calibration performance and clinical applicability for the EL model were observed. The EL model combining preoperative MR-based tumor radiomics and clinical data showed high accuracy and sensitivity in differentiating IEE from GBM preoperatively, which may potentially assist in clinical management of these brain tumors.
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Affiliation(s)
- Haoling He
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qianyan Long
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Liyan Li
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xueying Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuhong Qin
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Muliang Jiang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zeming Tan
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, California, USA
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3
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Qiao J, Liu B, Xin J, Shen S, Ma H, Pan S. Prediction of Prognosis and Response to Androgen Deprivation Therapy in Intermediate to High-Risk Prostate Cancer Using 18F-FDG PET/CT Radiomics. Acad Radiol 2024:S1076-6332(24)00420-3. [PMID: 39019687 DOI: 10.1016/j.acra.2024.06.034] [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: 04/22/2024] [Revised: 06/10/2024] [Accepted: 06/22/2024] [Indexed: 07/19/2024]
Abstract
RATIONALE AND OBJECTIVES This study aims to predict intermediate to high-risk prostate cancer (PCa) prognosis based on 18-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics. Additionally, subgroup analysis will be performed on the androgen deprivation therapy (ADT) group and the metastatic PCa group. MATERIALS AND METHODS In the retrospective analysis of 104 intermediate to high-risk PCa patients who underwent 18F-FDG PET/CT prior to treatment. The data set was divided into a training set (n = 72) and a testing set (n = 32). Two different PET/CT models were constructed using multivariate logistic regression with cross-validation: radiomics model A and an alternative ensemble learning-based model B. The superior model was then selected to develop a radiomics nomogram. Separate models were also developed for the ADT and metastatic PCa subgroups. RESULTS Model A, which integrates eight radiomics features showed excellent performance with an area under curve (AUC) of 0.844 in the training set and 0.804 in the testing set. The radiomics nomogram incorporating the radiomics score (radscore) from model A and the tumor-to-liver ratio (TLR) showed good prognostic accuracy in the testing set with an AUC of 0.827. In the subgroup analyses for endocrine therapy and metastatic cancer, the PET/CT radiomics model showed AUCs of 0.845 and 0.807 respectively, suggesting its potential effectiveness. CONCLUSION The study establishes the utility of the 18F-FDG PET/CT radiomics nomogram in predicting the prognosis of intermediate to high-risk PCa patients, indicating its potential for clinical application.
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Affiliation(s)
- Jianyi Qiao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Bitian Liu
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jun Xin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China; Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Siang Shen
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Han Ma
- Department of Nuclear Medicine, People's Hospital of Liaoning Province, Shenyang, China
| | - Shen Pan
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China; Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
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Mendes B, Domingues I, Santos J. Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions. J Clin Med 2024; 13:3907. [PMID: 38999473 PMCID: PMC11242211 DOI: 10.3390/jcm13133907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.
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Affiliation(s)
- Bruno Mendes
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Faculty of Engineering of the University of Porto (FEUP), R. Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Inês Domingues
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
| | - João Santos
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- School of Medicine and Biomedical Sciences (ICBAS), R. Jorge de Viterbo Ferreira 228, 4050-313 Porto, Portugal
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5
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Denijs FB, van Harten MJ, Meenderink JJL, Leenen RCA, Remmers S, Venderbos LDF, van den Bergh RCN, Beyer K, Roobol MJ. Risk calculators for the detection of prostate cancer: a systematic review. Prostate Cancer Prostatic Dis 2024:10.1038/s41391-024-00852-w. [PMID: 38830997 DOI: 10.1038/s41391-024-00852-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm. METHODS We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men. RESULTS We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93. CONCLUSIONS This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.
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Affiliation(s)
- Frederique B Denijs
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Meike J van Harten
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonas J L Meenderink
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Renée C A Leenen
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lionne D F Venderbos
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Roderick C N van den Bergh
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Katharina Beyer
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Monique J Roobol
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
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6
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Yin S, Ding N, Ji Y, Qiao Z, Yuan J, Chi J, Jin L. The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions. Acta Radiol 2024; 65:554-564. [PMID: 38623640 DOI: 10.1177/02841851241245970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
BACKGROUND Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery. PURPOSE To investigate the potential of various machine learning models, incorporating radiomics and deep transfer learning, in predicting the nature of cholesterol and adenomatous gallbladder polyps. MATERIAL AND METHODS A retrospective analysis was conducted on clinical and imaging data from 100 patients with cholesterol or adenomatous polyps confirmed by surgery and pathology at our hospital between September 2015 and February 2023. Preoperative contrast-enhanced CT radiomics combined with deep learning features were utilized, and t-tests and least absolute shrinkage and selection operator (LASSO) cross-validation were employed for feature selection. Subsequently, 11 machine learning algorithms were utilized to construct prediction models, and the area under the ROC curve (AUC), accuracy, and F1 measure were used to assess model performance, which was validated in a validation group. RESULTS The Logistic algorithm demonstrated the most effective prediction in identifying polyp properties based on 10 radiomics combined with deep learning features, achieving the highest AUC (0.85 in the validation group, 95% confidence interval = 0.68-1.0). In addition, the accuracy (0.83 in the validation group) and F1 measure (0.76 in the validation group) also indicated strong performance. CONCLUSION The machine learning radiomics combined with deep learning model based on enhanced CT proves valuable in predicting the characteristics of cholesterol and adenomatous gallbladder polyps. This approach provides a more reliable basis for preoperative diagnosis and treatment of these conditions.
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Affiliation(s)
- Shengnan Yin
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Ning Ding
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Yiding Ji
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Zhenguo Qiao
- Department of Gastroenterology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Jianmao Yuan
- Department of General Surgery, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Jing Chi
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Long Jin
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
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Castaldo R, Brancato V, Cavaliere C, Pecchia L, Illiano E, Costantini E, Ragozzino A, Salvatore M, Nicolai E, Franzese M. Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters. Front Oncol 2024; 14:1323247. [PMID: 38873254 PMCID: PMC11171723 DOI: 10.3389/fonc.2024.1323247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/01/2024] [Indexed: 06/15/2024] Open
Abstract
Introduction Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment. Methods Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age. Results The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2. Discussion This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.
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Affiliation(s)
- Rossana Castaldo
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | | | - Carlo Cavaliere
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Università Campus Bio-Medico Roma, Roma, Italy
- Campus Bio-Medico, Fondazione Policlinico Universitario, Roma, Italy
| | - Ester Illiano
- Adrology and Urogynecological Clinic, Santa Maria Terni Hospital, University of Perugia, Terni, Italy
| | - Elisabetta Costantini
- Adrology and Urogynecological Clinic, Santa Maria Terni Hospital, University of Perugia, Terni, Italy
| | - Alfonso Ragozzino
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Marco Salvatore
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Emanuele Nicolai
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Monica Franzese
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
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8
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Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, Hislop R, Pratt N, Steele JD, Kernohan N, Nabi G. Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging. Int J Mol Sci 2024; 25:5379. [PMID: 38791417 PMCID: PMC11121591 DOI: 10.3390/ijms25105379] [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: 04/13/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.
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Affiliation(s)
- Chidozie N. Ogbonnaya
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | | | - Abeer J. Alhussaini
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Robert Hislop
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - Norman Pratt
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - J. Douglas Steele
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Neil Kernohan
- Department of Pathology, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK;
| | - Ghulam Nabi
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
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9
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Kaneko M, Magoulianitis V, Ramacciotti LS, Raman A, Paralkar D, Chen A, Chu TN, Yang Y, Xue J, Yang J, Liu J, Jadvar DS, Gill K, Cacciamani GE, Nikias CL, Duddalwar V, Jay Kuo CC, Gill IS, Abreu AL. The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics. Urol Clin North Am 2024; 51:1-13. [PMID: 37945095 DOI: 10.1016/j.ucl.2023.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution.
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Affiliation(s)
- Masatomo Kaneko
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer; Department of Urology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Vasileios Magoulianitis
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Lorenzo Storino Ramacciotti
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Alex Raman
- Western University of Health Sciences. Pomona, CA, USA
| | - Divyangi Paralkar
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Andrew Chen
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Timothy N Chu
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Yijing Yang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jintang Xue
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jiaxin Yang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jinyuan Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Donya S Jadvar
- Dornsife School of Letters and Science, University of Southern California, Los Angeles, CA, USA
| | - Karanvir Gill
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer
| | - Giovanni E Cacciamani
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chrysostomos L Nikias
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - C-C Jay Kuo
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Inderbir S Gill
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andre Luis Abreu
- USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; USC Institute of Urology, Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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10
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Palumbo P, Martinese A, Antenucci MR, Granata V, Fusco R, De Muzio F, Brunese MC, Bicci E, Bruno A, Bruno F, Giovagnoni A, Gandolfo N, Miele V, Di Cesare E, Manetta R. Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer. Gland Surg 2023; 12:1806-1822. [PMID: 38229839 PMCID: PMC10788566 DOI: 10.21037/gs-23-53] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 11/09/2023] [Indexed: 01/18/2024]
Abstract
Background and Objective In recent years, magnetic resonance imaging (MRI) has shown excellent results in the study of the prostate gland. MRI has indeed shown to be advantageous in the prostate cancer (PCa) detection, as in guiding targeting biopsy, improving its diagnostic yield. Although current acquisition protocols provide for multiparametric acquisition, recent evidence has shown that biparametric protocols can be non-inferior in PCa detection. Diffusion-weighted imaging (DWI) sequence, in particular, plays a key role, particularly in the peripheral zone which accounts for the larger part of the prostate. High b-values are generally recommended, although with the possibility of obtaining non-Gaussian diffusion effects, which requires a more sophisticated model for the analysis, namely through the diffusion kurtosis imaging (DKI). Purpose of this narrative review was to analyze the current applications and clinical evidence regarding the use of DKI with a main focus on PCa detection, also in comparison with DWI. Methods This narrative review synthesized the findings of literature retrieved from main researches, narrative and systematic reviews, and meta-analyses obtained from PubMed. Key Content and Findings DKI analyses the non-Gaussian water diffusivity and describe the effect of signal intensity decay related to high b-value through two main metrics (Dapp and Kapp). Differently from DWI-apparent diffusion coefficient (DWI-ADC) which reflects only water restriction outside of cells, DKI metrics are supposed to represent also the direct interaction of water molecules with cell membranes and intracellular compounds. This review describes current evidence on ADC and DKI metrics in clinical imaging, and finally collect the results derived from the main articles focused on DWI and DKI models in detecting PCa. Conclusions DKI advantages, compared to conventional ADC analysis, still remain controversial. Wider application and greater technical knowledge of DKI, however, may help in proving its intrinsic validity in the field of oncology and therefore in the study of clinically significant PCa. Finally, a deep understanding of DKI is important for radiologists to better understand what Kapp and Dapp mean in the context of different cancer and how these metrics may vary specifically in PCa imaging.
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Affiliation(s)
- Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, L’Aquila, Italy
| | - Andrea Martinese
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, L’Aquila, Italy
| | - Maria Rosaria Antenucci
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, L’Aquila, Italy
| | - Vincenza Granata
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, Naples, Italy
| | | | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, Campobasso, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, Campobasso, Italy
| | - Eleonora Bicci
- Department of Emergency Radiology, University Hospital Careggi, Florence, Italy
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Ancona, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, University Hospital Careggi, Florence, Italy
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
| | - Rosa Manetta
- Radiology Unit, San Salvatore Hospital, Abruzzo Health Unit 1, L’Aquila, Italy
- Prostate Unit, San Salvatore Hospital, Abruzzo Health Unit 1, L’Aquila, Italy
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11
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Yang Q, Li Q, Li N, Wang D, Niu S, Tang P, Xiao J, Zhao J, Wang P, Luo Y, Tang J. Radiotranscriptomics identified new mRNAs and miRNA markers for distinguishing prostate cancer from benign prostatic hyperplasia. Cancer Med 2023; 12:21694-21708. [PMID: 37987209 PMCID: PMC10757143 DOI: 10.1002/cam4.6728] [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: 04/30/2023] [Revised: 10/30/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
The present study investigated ultrasound (US) phenotypes reflecting prostate cancer (PCa)-related genetic mutations. Herein, integration of radiotranscriptomic data, US and contrast-enhanced ultrasound (CEUS) radiomic images, and RNA sequencing was performed with the aim of significantly improving the accuracy of PCa prognosis. We performed radiotranscriptomic analysis of clinical, imaging, and two genomic (mRNA and microRNA expression) datasets from 48 and 22 men with PCa and benign prostatic hyperplasia (BPH), respectively. Twenty-three US texture features and four microvascular perfusion features were associated with various patterns of 52 differentially expressed genes related to PCa (p < 0.05); 17 overexpressed genes were associated with two key texture features. Twelve overexpressed genes were identified using microvascular perfusion features. Furthermore, mRNA and miRNA biomarkers could be used to distinguish between PCa and BPH. Compared with RNA sequencing, B-mode and CEUS features reflected genomic alterations associated with hormone receptor status, angiogenesis, and prognosis in patients with PCa. These findings indicate the potential of US to assess biomarker levels in patients with PCa.
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Affiliation(s)
- Qian Yang
- Department of Ultrasound, Air Force Medical CenterPLA, Air Force Military Medical UniversityBeijingChina
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
| | - Qiuyang Li
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
| | - Nan Li
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
| | - Dingyi Wang
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
| | - Shaoxi Niu
- Department of Urology, First Medical CenterChinese PLA General HospitalBeijingChina
| | - Peng Tang
- Department of Orthopedics, China Rehabilitation Research CenterBeijing Charity HospitalBeijingChina
| | - Jing Xiao
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
| | - Jiahang Zhao
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
| | - Pei Wang
- Department of Ultrasound Diagnosis and Treatment CenterXi'an International Medical Center HospitalXianChina
| | - Yukun Luo
- Department of Ultrasound, Air Force Medical CenterPLA, Air Force Military Medical UniversityBeijingChina
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
| | - Jie Tang
- Department of UltrasoundFirst Medical Center, Chinese PLA General HospitalBeijingChina
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12
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Zhu X, Shao L, Liu Z, Liu Z, He J, Liu J, Ping H, Lu J. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer. J Zhejiang Univ Sci B 2023; 24:663-681. [PMID: 37551554 PMCID: PMC10423970 DOI: 10.1631/jzus.b2200619] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/11/2023] [Indexed: 08/09/2023]
Abstract
Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.
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Affiliation(s)
- Xuehua Zhu
- Department of Urology, Peking University Third Hospital, Beijing 100191, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100080, China
| | - Zenan Liu
- Department of Urology, Peking University Third Hospital, Beijing 100191, China
| | - Jide He
- Department of Urology, Peking University Third Hospital, Beijing 100191, China
| | - Jiangang Liu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing 100191, China
| | - Hao Ping
- Department of Urology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Beijing 100191, China.
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13
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Liu Y. Comparison of Magnetic Resonance Imaging-Based Radiomics Features with Nomogram for Prediction of Prostate Cancer Invasion. Int J Gen Med 2023; 16:3043-3051. [PMID: 37485455 PMCID: PMC10361087 DOI: 10.2147/ijgm.s419039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
Objective To explore the value of the magnetic resonance imaging (MRI) radiomics model in predicting prostate cancer (PCa) invasion. Methods Clinical data of 86 pathologically confirmed PCa patients in our hospital were collected, including 44 cases in the invasive group and 42 cases in the non-invasive group. All patients underwent MRI examinations, and the same parameters were used. The lesion area was manually delineated and the radiomics features were extracted from T2WI. The radiomics signature based on LASSO regression was established. Besides, logistic regression was used to identify independent clinical predictors, and a combined model incorporating the radiomics signature and independent clinical risk factor was constructed. Finally, the receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) was performed to compare the prediction efficiency and clinical benefit of each model. Results A total of 867 radiomics features were obtained, and six of them were incorporated into the radiomics model. Multivariate logistic regression analysis exhibited the Gleason score as an independent clinical risk factor for PCa invasion. ROC results showed that the performance of the radiomics model was comparable to that of the clinical-radiomics model in predicting PCa invasion, and it was better than that of the single Gleason score. DCA also confirmed the considerable clinical application value of the radiomics and the clinical-radiomics models. Conclusion As a simple, non-invasive, and efficient method, the radiomics model has important predictive value for PCa invasion.
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Affiliation(s)
- Yang Liu
- X-Ray Department, The No.1 People’s Hospital of Huzhou, Huzhou, Zhejiang, 313000, People’s Republic of China
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14
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Qi X, Wang K, Feng B, Sun X, Yang J, Hu Z, Zhang M, Lv C, Jin L, Zhou L, Wang Z, Yao J. Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer. Front Oncol 2023; 13:1157949. [PMID: 37260984 PMCID: PMC10227569 DOI: 10.3389/fonc.2023.1157949] [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: 02/08/2023] [Accepted: 05/04/2023] [Indexed: 06/02/2023] Open
Abstract
Objective To establish machine learning (ML) prediction models for prostate cancer (PCa) using transrectal ultrasound videos and multi-parametric magnetic resonance imaging (mpMRI) and compare their diagnostic performance. Materials and methods We systematically collated the data of 383 patients, including 187 with PCa and 196 with benign lesions. Of them, 307 patients (150 with PCa and 157 with benign lesions) were randomly selected to train and validate the ML models, 76 patients were used as test set. B-Ultrasound videos (BUS), mpMRI T2 sequence (T2), and ADC sequence (ADC) were obtained from all patients. We extracted 851 features of each patient in the BUS, T2, and ADC groups and used a t-test, the Mann-Whitney U test, and LASSO regression to screen the features. Support vector machine (SVM), random forest (RF), adaptive boosting (ADB), and gradient boosting machine (GBM) models were used to establish radiomics models. In addition, we fused the features screened via LASSO regression from three groups as new features and rebuilt ML models. The performance of the ML models in diagnosing PCa in the BUS, T2, ADC, and fusion groups was compared using the area under the ROC curve (AUC), sensitivity, specificity, and accuracy. Results In the test cohort, the AUC of each model in the ADC group was higher than that of in the.BUS and T2 groups. Among the models, the RF model had the best diagnostic performance, with an AUC of 0.85, sensitivity of 0.78 (0.61-0.89), specificity of 0.84 (0.69-0.94), and accuracy of 0.83 (0.66-0.93). The SVM model in both the BUS and T2 groups performed best. Based on the features screened in the BUS, T2, and ADC groups fused to construct the models, the SVM model was found to perform best, with an AUC of 0.87, sensitivity of 0.73 (0.56-0.86), specificity of 0.79 (0.63-0.90), and accuracy of 0.77 (0.59-0.89). The difference in the results was statistically significant (p<0.05). Conclusion The ML prediction models had a good diagnostic ability for PCa. Among them, the SVM model in the fusion group showed the best performance in diagnosing PCa. These prediction models can help radiologists make better diagnoses.
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Affiliation(s)
- Xiaoyang Qi
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Bojian Feng
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
| | - Xingbo Sun
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Jie Yang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Cheng Lv
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Liyuan Jin
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Lingyan Zhou
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
| | - Zhengping Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Jincao Yao
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
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15
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Zhang M, Liu Y, Yao J, Wang K, Tu J, Hu Z, Jin Y, Du Y, Sun X, Chen L, Wang Z. Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer. Front Endocrinol (Lausanne) 2023; 14:1137322. [PMID: 36967794 PMCID: PMC10031096 DOI: 10.3389/fendo.2023.1137322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
Objective To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer. Methods Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC). Results Independent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855. Conclusion Establishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.
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Affiliation(s)
- Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yuanzhen Liu
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jincao Yao
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jing Tu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yun Jin
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yue Du
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xingbo Sun
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Liyu Chen
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhengping Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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16
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Zhang Y, Li W, Zhang Z, Xue Y, Liu YL, Nie K, Su MY, Ye Q. Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics. Med Biol Eng Comput 2023; 61:757-771. [PMID: 36598674 PMCID: PMC10548872 DOI: 10.1007/s11517-022-02759-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023]
Abstract
Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted in radiologists' visual evaluation. More sophisticated computer algorithms are needed to extract the higher-order information. The purpose of this study was to apply a new deep learning algorithm, the bi-directional convolutional long short-term memory (CLSTM) network, and the radiomics analysis for differential diagnosis of PCa and benign prostatic hyperplasia (BPH). To systematically investigate the optimal amount of peritumoral tissue for improving diagnosis, a total of 9 ROIs were delineated by using 3 different methods. The results showed that bi-directional CLSTM with ± 20% region growing peritumoral ROI achieved the mean AUC of 0.89, better than the mean AUC of 0.84 by using the tumor alone without any peritumoral tissue (p = 0.25, not significant). For all 9 ROIs, deep learning had higher AUC than radiomics, but only reaching the significant difference for ± 20% region growing peritumoral ROI (0.89 vs. 0.79, p = 0.04). In conclusion, the kinetic information extracted from DCE-MRI using bi-directional CLSTM may provide helpful supplementary information for diagnosis of PCa.
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Affiliation(s)
- Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA
| | - Weikang Li
- Department of Radiology, The Children's Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhao Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yingnan Xue
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA.
| | - Qiong Ye
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, 230031, Anhui, People's Republic of China.
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17
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Yang Y, Zhang W, Wan L, Tang Z, Zhang Q, Bai Y, Zhang D. Construction and validation of a clinical predictive nomogram for intraductal carcinoma of the prostate based on Chinese multicenter clinical data. Front Oncol 2022; 12:1074478. [PMID: 36591521 PMCID: PMC9798232 DOI: 10.3389/fonc.2022.1074478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction Intraductal carcinoma of the prostate (IDC-P) is a special pathological type of prostate cancer that is highly aggressive with poor prognostic outcomes. Objective To establish an effective predictive model for predicting IDC-P. Methods Data for 3185 patients diagnosed with prostate cancer at three medical centers in China from October 2012 to April 2022 were retrospectively analyzed. One cohort (G cohort) consisting of 2384 patients from Zhejiang Provincial People's Hospital was selected for construction (Ga cohort) and internal validate (Gb cohort)of the model. Another cohort (I cohort) with 344 patients from Quzhou People's Hospital and 430 patients from Jiaxing Second People's Hospital was used for external validation. Univariate and multivariate binary logistic regression analyses were performed to identify the independent predictors. Then, the selected predictors were then used to establish the predictive nomogram. The apparent performance of the model was evaluated via externally validated. Decision curve analysis was also performed to assess the clinical utility of the developed model. Results Univariate and multivariate logistic regression analyses showed that alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase were independent predictors of IDC-P. Therefore, a predictive nomogram of IDC-P was constructed. The nomogram had a good discriminatory power (AUC = 0.794). Internal validation (AUC = 0.819)and external validation (AUC = 0.903) also revealed a good predictive ability. Calibration curves showed good agreement between the predicted and observed incidences of IDC-P. Conclusion We developed a clinical predictive model composed of alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase (LDH) with a high precision and universality. This model provides a novel calculator for predicting the diagnosis of IDC-P and different treatment options for patients at an early stage.
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Affiliation(s)
- YunKai Yang
- Urology & Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China,The 2nd Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Wei Zhang
- Zhejiang Provincial People’s Hospital, Qingdao University, Shandong, Qingdao, China
| | - LiJun Wan
- Department of Urology, Quzhou People’s Hospital, Quzhou, Zhejiang, China
| | - ZhiLing Tang
- Department of Urology, Jiaxing Second People’s Hospital, Jiaxing, Zhejiang, China
| | - Qi Zhang
- Urology & Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China
| | - YuChen Bai
- Urology & Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China,*Correspondence: YuChen Bai, ; DaHong Zhang,
| | - DaHong Zhang
- Urology & Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China,The 2nd Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China,*Correspondence: YuChen Bai, ; DaHong Zhang,
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MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies. Diagnostics (Basel) 2022; 12:diagnostics12123005. [PMID: 36553012 PMCID: PMC9776817 DOI: 10.3390/diagnostics12123005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/04/2022] Open
Abstract
OBJECTIVE The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters. METHODS In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. RESULTS The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. CONCLUSION The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.
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Jia Y, Quan S, Ren J, Wu H, Liu A, Gao Y, Hao F, Yang Z, Zhang T, Hu H. MRI radiomics predicts progression-free survival in prostate cancer. Front Oncol 2022; 12:974257. [PMID: 36110963 PMCID: PMC9468743 DOI: 10.3389/fonc.2022.974257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/02/2022] [Indexed: 01/31/2023] Open
Abstract
Objective To assess the predictive value of magnetic resonance imaging (MRI) radiomics for progression-free survival (PFS) in patients with prostate cancer (PCa). Methods 191 patients with prostate cancer confirmed by puncture biopsy or surgical pathology were included in this retrospective study, including 133 in the training group and 58 in the validation group. All patients underwent T2WI and DWI serial scans. Three radiomics models were constructed using univariate logistic regression and Gradient Boosting Decision Tree(GBDT) for feature screening, followed by Cox risk regression to construct a mixed model combining radiomics features and clinicopathological risk factors and to draw a nomogram. The performance of the models was evaluated by receiver operating characteristic curve (ROC), calibration curve and decision curve analysis. The Kaplan-Meier method was applied for survival analysis. Results Compared with the radiomics model, the hybrid model consisting of a combination of radiomics features and clinical data performed the best in predicting PFS in PCa patients, with AUCs of 0.926 and 0.917 in the training and validation groups, respectively. Decision curve analysis showed that the radiomics nomogram had good clinical application and the calibration curve proved to have good stability. Survival curves showed that PFS was shorter in the high-risk group than in the low-risk group. Conclusion The hybrid model constructed from radiomics and clinical data showed excellent performance in predicting PFS in prostate cancer patients. The nomogram provides a non-invasive diagnostic tool for risk stratification of clinical patients.
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Affiliation(s)
- Yushan Jia
- Affiliated Hospital, Inner Mongolia Medical University, Hohhot, China
| | - Shuai Quan
- Department of Pharmaceuticals Diagnosis, GE Healthcare (China), Shanghai, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare (China), Shanghai, China
| | - Hui Wu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China,*Correspondence: Hui Wu, ; Aishi Liu,
| | - Aishi Liu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China,*Correspondence: Hui Wu, ; Aishi Liu,
| | - Yang Gao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Fene Hao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Zhenxing Yang
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Tong Zhang
- Affiliated Hospital, Inner Mongolia Medical University, Hohhot, China
| | - He Hu
- Affiliated Hospital, Inner Mongolia Medical University, Hohhot, China
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20
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Gupta K, Perchik JD, Fang AM, Porter KK, Rais-Bahrami S. Augmenting prostate magnetic resonance imaging reporting to incorporate diagnostic recommendations based upon clinical risk calculators. World J Radiol 2022; 14:249-255. [PMID: 36160831 PMCID: PMC9453318 DOI: 10.4329/wjr.v14.i8.249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/27/2022] [Accepted: 07/25/2022] [Indexed: 02/08/2023] Open
Abstract
Risk calculators have offered a viable tool for clinicians to stratify patients at risk of prostate cancer (PCa) and to mitigate the low sensitivity and specificity of screening prostate specific antigen (PSA). While initially based on clinical and demographic data, incorporation of multiparametric magnetic resonance imaging (MRI) and the validated prostate imaging reporting and data system suspicion scoring system has standardized and improved risk stratification beyond the use of PSA and patient parameters alone. Biopsy-naïve patients with lower risk profiles for harboring clinically significant PCa are often subjected to uncomfortable, invasive, and potentially unnecessary prostate biopsy procedures. Incorporating risk calculator data into prostate MRI reports can broaden the role of radiologists, improve communication with clinicians primarily managing these patients, and help guide clinical care in directing the screening, detection, and risk stratification of PCa.
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Affiliation(s)
- Karisma Gupta
- Department of Radiology, University of Washington, Seattle, WA 98195, United States
| | - Jordan D Perchik
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35233, United States
| | - Andrew M Fang
- Department of Urology, University of Alabama at Birmingham, Birmingham, AL 35233, United States
| | - Kristin K Porter
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35233, United States
| | - Soroush Rais-Bahrami
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35233, United States
- Department of Urology, University of Alabama at Birmingham, Birmingham, AL 35233, United States
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, United States
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21
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Li L, Gu L, Kang B, Yang J, Wu Y, Liu H, Lai S, Wu X, Jiang J. Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions. Front Oncol 2022; 12:934108. [PMID: 35865467 PMCID: PMC9295912 DOI: 10.3389/fonc.2022.934108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/07/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo compare the performance of different imaging classifiers in the prospective diagnosis of prostate diseases based on multiparameter MRI.MethodsA total of 238 patients with pathological outcomes were enrolled from September 2019 to July 2021, including 142 in the training set and 96 in the test set. After the regions of interest were manually segmented, decision tree (DT), Gaussian naive Bayes (GNB), XGBoost, logistic regression, random forest (RF) and support vector machine classifier (SVC) models were established on the training set and tested on the independent test set. The prospective diagnostic performance of each classifier was compared by using the AUC, F1-score and Brier score.ResultsIn the patient-based data set, the top three classifiers of combined sequences in terms of the AUC were logistic regression (0.865), RF (0.862), and DT (0.852); RF “was significantly different from the other two classifiers (P =0.022, P =0.005), while logistic regression and DT had no statistical significance (P =0.802). In the lesions-based data set, the top three classifiers of combined sequences in terms of the AUC were RF (0.931), logistic regression (0.922) and GNB (0.922). These three classifiers were significantly different from.ConclusionThe results of this experiment show that radiomics has a high diagnostic efficiency for prostate lesions. The RF classifier generally performed better overall than the other classifiers in the experiment. The XGBoost and logistic regression models also had high classification value in the lesions-based data set.
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Affiliation(s)
- Linghao Li
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Lili Gu
- Department of Pain, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Bin Kang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jiaojiao Yang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Ying Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Hao Liu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Shasha Lai
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Xueting Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jian Jiang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
- *Correspondence: Jian Jiang,
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22
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Yang L, Li Z, Liang X, Xu J, Cai Y, Huang C, Zhang M, Yao J, Song B. Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion. Front Oncol 2022; 12:934291. [PMID: 35837116 PMCID: PMC9274129 DOI: 10.3389/fonc.2022.934291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To assess the association of radiomics features based on multiparametric MRI (mpMRI) with the proportion of intraductal carcinoma of prostate (IDC-P) and validate the predictive models. Materials and Methods We retrospectively included pre-treatment MR images of prostate cancer (PCa) with IDC components of high proportion (≥10%, hpIDC-P), low proportion (<10%, lpIDC-P), and pure acinar adenocarcinoma (PAC) from our institution for training and internal validation and cooperated cohort for external validation. Normalized images of T2WI, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) map, and dynamic contrast enhanced (DCE) sequences were used for radiomics modeling. The clinical model was built based on serum total prostate specific antigen (tPSA) and Gleason score (GS), and the integrated model was the combination of Rad-score and clinicopathological data. The discrimination ability was assessed by area under the receiver operating characteristic curve (ROC-AUC) in the internal and external validation sets and compared by DeLong test. Results Overall, 97 patients with hpIDC-P, 87 lpIDC-P, and 78 PAC were included for training and internal validation, and 11, 16, and 19 patients for external validation. The integrated model for predicting hpIDC-P got the best ROC-AUC of 0.88 (95%CI = 0.83-0.93) in internal and 0.86 (95%CI = 0.72-1.0) in external validation, which both outperformed clinical models (AUC=0.78, 95% CI = 0.72-0.85, AUC=0.69, 95% CI = 0.5-0.85, respectively) based solely on GS, and the radiomics model (AUC=0.85, 95% CI = 0.79-0.91) was slightly inferior to the integrated model and better than the clinical model in internal dataset. The integrated model for predicting lpIDC-P outperformed both radiomics and clinical models in the internal dataset, while slightly inferior to the integrated model for predicting hpIDC-P. Conclusions Radiomics signature improved differentiation of both hpIDC-P and lpIDC-P versus PAC when compared with the clinical model based on Gleason score, and was validated in an external cohort.
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Affiliation(s)
- Ling Yang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Zhengyan Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xu Liang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yusen Cai
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Mengni Zhang
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Jin Yao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Sheng A, Zhou P, Ye Y, Sun K, Yang Z. Diagnostic Efficacy of CT Radiomic Features in Pulmonary Invasive Mucinous Adenocarcinoma. SCANNING 2022; 2022:5314225. [PMID: 35832299 PMCID: PMC9252846 DOI: 10.1155/2022/5314225] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/06/2022] [Accepted: 06/14/2022] [Indexed: 05/14/2023]
Abstract
In order to solve the problem of the effect of CT images on the diagnosis of lungs, the authors proposed a method for the diagnosis of invasive mucinous adenocarcinoma of the lungs based on CT radiomic features, and the modified method is found by reviewing past cases: among the 34 cases of primary pulmonary lymphoma, 12 cases were nodular mass type, 19 cases were nonnodular mass type, and 3 cases were mixed type; 13 cases involved bilateral lung lobes, 7 cases involved right lung, and 4 cases involved left lung example. There were 17 cases of tumor consolidation density shadow, 17 cases of mixed density shadow, the average CT value was about 32HU, 15 cases of cavitation sign, 6 cases of cavity, 9 cases of angiography sign, 30 cases of air bronchus sign, 22 cases of bronchiectasis, bronchial stenosis or amputation in 8 cases, pleural effusion in 12 cases, lymph node enlargement in 15 cases, and pleural metastasis in 2 cases. The final pathological results included 24 cases of membrane-associated lymphoid tissue (MALT) lymphoma, 9 cases of diffuse large B-cell lymphoma (DLBCL), and 1 case of T-cell lymphoma. The CT manifestations of primary pulmonary lymphoma (PPL) are diverse and do not have obvious specificity, the imaging manifestations are correlated with pathological types, and air bronchial signs, bronchiectasis, angiography signs, and other signs are used for the diagnosis of PPL. This is of great significance for the diagnosis of PPL.
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Affiliation(s)
- Aizhu Sheng
- Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Zhejiang 315000, China
| | - Pengfei Zhou
- Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Zhejiang 315000, China
| | - Yizhai Ye
- Department of Radiology, Ninghai First Hospital, Ningbo, Zhejiang 315600, China
| | - Keda Sun
- Department of Radiology, No. 2 Hospital of Yinzhou District, Ningbo, Zhejiang 315100, China
| | - Zhenhua Yang
- Department of Thoracic Surgery, Hwa Mei Hospital, University of Chinese Academy of Sciences, Zhejiang 315000, China
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24
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Gui S, Lan M, Wang C, Nie S, Fan B. Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia. Front Oncol 2022; 12:859625. [PMID: 35494065 PMCID: PMC9047828 DOI: 10.3389/fonc.2022.859625] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022] Open
Abstract
Objective Prostate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI)-derived radiomic nomogram based on T2-weighted images (T2WI) in differentiating prostate cancer and hyperplasia. Materials and Methods One hundred forty-six patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected. We manually delineated T2WI of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected using the LASSO algorithm, and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish a radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram. Finally, the clinical application value of the nomogram was evaluated by decision curve analysis. Results The PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC = 0.85; 95% CI: 0.77–0.92) and testing set (AUC = 0.84; 95% CI: 0.72–0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC = 0.91; 95% CI: 0.85–0.97) and testing set (AUC = 0.90; 95% CI: 0.81–0.99). The decision curve showed the clinical application value of the radiomic nomogram. Conclusion The radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia. It also provides suggestions for further clinical events.
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Affiliation(s)
- Shaogao Gui
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Min Lan
- Department of Orthopedics, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Chaoxiong Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Si Nie
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Si Nie, ; Bing Fan,
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Si Nie, ; Bing Fan,
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25
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Sushentsev N, Moreira Da Silva N, Yeung M, Barrett T, Sala E, Roberts M, Rundo L. Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review. Insights Imaging 2022; 13:59. [PMID: 35347462 PMCID: PMC8960511 DOI: 10.1186/s13244-022-01199-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/24/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES We systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automated traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolent PCa (iPCa) and benign conditions. METHODS We performed a computerised bibliographic search of studies indexed in MEDLINE/PubMed, arXiv, medRxiv, and bioRxiv between 1 January 2016 and 31 July 2021. Two reviewers performed the title/abstract and full-text screening. The remaining papers were screened by four reviewers using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for DL studies and Radiomics Quality Score (RQS) for TML studies. Papers that fulfilled the pre-defined screening requirements underwent full CLAIM/RQS evaluation alongside the risk of bias assessment using QUADAS-2, both conducted by the same four reviewers. Standard measures of discrimination were extracted for the developed predictive models. RESULTS 17/28 papers (five DL and twelve TML) passed the quality screening and were subject to a full CLAIM/RQS/QUADAS-2 assessment, which revealed a substantial study heterogeneity that precluded us from performing quantitative analysis as part of this review. The mean RQS of TML papers was 11/36, and a total of five papers had a high risk of bias. AUCs of DL and TML papers with low risk of bias ranged between 0.80-0.89 and 0.75-0.88, respectively. CONCLUSION We observed comparable performance of the two classes of AI methods and identified a number of common methodological limitations and biases that future studies will need to address to ensure the generalisability of the developed models.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
| | | | - Michael Yeung
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Tristan Barrett
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Lucida Medical Ltd, Biomedical Innovation Hub, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, The Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK
- Oncology R&D, AstraZeneca, Cambridge, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Lucida Medical Ltd, Biomedical Innovation Hub, University of Cambridge, Cambridge, UK
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, SA, Italy
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Pirzad Mashak N, Akbarizadeh G, Farshidi E. A new approach for data augmentation in a deep neural network to implement a monitoring system for detecting prostate cancer in MRI images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Prostate cancer is one of the most common cancers in men, which takes many victims every year due to its latent symptoms. Thus, early diagnosis of the extent of the lesion can help the physician and the patient in the treatment process. Nowadays, detection and labeling of objects in medical images has become especially important. In this article, the prostate gland is first detected in T2 W MRI images by the Faster R-CNN network based on the AlexNet architecture and separated from the rest of the image. Using the Faster R-CNN network in the separation phase, the accuracy will increase as this network is a model of CNN-based target detection networks and is functionally coordinated with the subsequent CNN network. Meanwhile, the problem of insufficient data with the data augmentation method was corrected in the preprocessing stage, for which different filters were used. Use of different filters to increase the data instead of the usual augmentation methods would eliminate the preprocessing stage. Also, with the presence of raw images in the next steps, it was proven that there was no need for a preprocessing step and the main images could also be the input data. By eliminating the preprocessing step, the response speed increased. Then, in order to classify benign and malignant cancer images, two deep learning architectures were used under the supervision of ResNet18 and GoogleNet. Then, by calculating the Confusion Matrix parameters and drawing the ROC diagram, the capability of this process was measured. By obtaining Accuracy = 95.7%, DSC = 96.77% and AUC = 99.17%, The results revealed that this method could outperform other well-known methods in this field (DSC = 95%) and (AUC = 91%).
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Affiliation(s)
- Neda Pirzad Mashak
- Department of Electrical Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
| | - Gholamreza Akbarizadeh
- Department of Electrical Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Ebrahim Farshidi
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
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27
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Tao T, Wang C, Liu W, Yuan L, Ge Q, Zhang L, He B, Wang L, Wang L, Xiang C, Wang H, Chen S, Xiao J. Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data. Front Oncol 2022; 11:811866. [PMID: 35127526 PMCID: PMC8814531 DOI: 10.3389/fonc.2021.811866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/28/2021] [Indexed: 12/20/2022] Open
Abstract
Objectives Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy. Materials and Methods The data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed. Results The results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected. Conclusions In this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.
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Affiliation(s)
- Tao Tao
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Changming Wang
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Weiyong Liu
- Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Lei Yuan
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Qingyu Ge
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Lang Zhang
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| | - Biming He
- Department of Urology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.,Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Lei Wang
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ling Wang
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Caiping Xiang
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Haifeng Wang
- Department of Urology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.,Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Shuqiu Chen
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| | - Jun Xiao
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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Chen T, Zhang Z, Tan S, Zhang Y, Wei C, Wang S, Zhao W, Qian X, Zhou Z, Shen J, Dai Y, Hu J. MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI. Front Oncol 2022; 11:792456. [PMID: 35127499 PMCID: PMC8810653 DOI: 10.3389/fonc.2021.792456] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 12/20/2021] [Indexed: 12/18/2022] Open
Abstract
PurposeTo compare the performance of radiomics to that of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 scoring system in the detection of clinically significant prostate cancer (csPCa) based on biparametric magnetic resonance imaging (bpMRI) vs. multiparametric MRI (mpMRI).MethodsA total of 204 patients with pathological results were enrolled between January 2018 and December 2019, with 142 patients in the training cohort and 62 patients in the testing cohort. The radiomics model was compared with the PI-RADS v2.1 for the diagnosis of csPCa based on bpMRI and mpMRI by using receiver operating characteristic (ROC) curve analysis.ResultsThe radiomics model based on bpMRI and mpMRI signatures showed high predictive efficiency but with no significant differences (AUC = 0.975 vs 0.981, p=0.687 in the training cohort, and 0.953 vs 0.968, p=0.287 in the testing cohort, respectively). In addition, the radiomics model outperformed the PI-RADS v2.1 in the diagnosis of csPCa regardless of whether bpMRI (AUC = 0.975 vs. 0.871, p= 0.030 for the training cohort and AUC = 0.953 vs. 0.853, P = 0.024 for the testing cohort) or mpMRI (AUC = 0.981 vs. 0.880, p= 0.030 for the training cohort and AUC = 0.968 vs. 0.863, P = 0.016 for the testing cohort) was incorporated.ConclusionsOur study suggests the performance of bpMRI- and mpMRI-based radiomics models show no significant difference, which indicates that omitting DCE imaging in radiomics can simplify the process of analysis. Adding radiomics to PI-RADS v2.1 may improve the performance to predict csPCa.
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Affiliation(s)
- Tong Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhiyuan Zhang
- School of Medical Imaging, Biomedical Engineering, Xuzhou Medical University, Xuzhou, China
| | - Shuangxiu Tan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Department of Ultrasound, Nanjing Drum Tower Hospital, Nanjing Medical School, Nanjing, China
| | - Yueyue Zhang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Chaogang Wei
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Shan Wang
- Department of Radiology, Jiangsu Jiangyin People’s Hospital, Jiangyin, China
| | - Wenlu Zhao
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, China
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Junkang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Imaging Medicine, Soochow University, Suzhou, China
- *Correspondence: Junkang Shen, ; Yakang Dai, ; Jisu Hu,
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- *Correspondence: Junkang Shen, ; Yakang Dai, ; Jisu Hu,
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, China
- *Correspondence: Junkang Shen, ; Yakang Dai, ; Jisu Hu,
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Nuo Y, Li A, Yang L, Xue H, Wang F, Wang L. Efficacy of 68Ga-PSMA-11 PET/CT with biparametric MRI in diagnosing prostate cancer and predicting risk stratification: a comparative study. Quant Imaging Med Surg 2022; 12:53-65. [PMID: 34993060 DOI: 10.21037/qims-21-80] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 05/26/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND This retrospective study aimed to investigate the efficacy of the combined application of biparametric magnetic resonance imaging (bpMRI) and 68Ga-PSMA-11 positron emission computed tomography/computed tomography (bpMRI/PET) in the qualitative diagnosis of intermediate- to high-risk prostate cancer (PCa). METHODS The 105 patients with suspected PCa included in the study underwent bpMRI and PET/CT. BpMRI examinations included conventional sequences and diffusion-weighted imaging (DWI) sequences. Major lesions were qualitatively diagnosed according to the Prostate Imaging Reporting and Data System (PI-RADS). A PET/CT scan was started 60 min after intravenous 68Ga-PSMA-11 injection. The area with the highest radioactivity on PET/CT images was defined as the major lesion, and the maximum standard uptake value (SUVmax) was measured. All cases were confirmed by biopsy and pathology. Receiver operating characteristic curve (ROC) analysis was performed on the data to calculate sensitivity, specificity, and the Youden index. RESULTS Of the 105 patients, 68 patients were diagnosed with PCa, and 37 patients had benign prostatic lesions. With a PI-RADS score ≥3 as the diagnostic threshold, the accuracy of bpMRI in identifying benign and malignant prostate lesions was similar to that of PET/CT (SUVmax threshold ≥10.9), and the Youden indices were 0.60 and 0.64, respectively. The sensitivity and specificity of bpMRI in the differential diagnosis of intermediate- to high-risk PCa versus low-risk PCa or benign lesions were 63% and 88%, respectively, and the Youden index was 0.51. With an SUVmax ≥12.9 as the diagnostic threshold, the sensitivity and specificity of PET/CT in the differential diagnosis of intermediate- to high-risk PCa versus low-risk PCa or benign lesions were 74% and 94%, respectively, and the Youden index was 0.68. The sensitivity and specificity of bpMRI/PET in diagnosing PCa were 94% and 81%, respectively, and the Youden index was 0.75. The sensitivity and specificity of bpMRI/PET in the differential diagnosis of intermediate- to high-risk PCa versus low-risk PCa or benign lesions were 80% and 88%, respectively, and the Youden index was 0.68. CONCLUSIONS The combined application of bpMRI and PET improves the accuracy of the qualitative diagnosis of prostate lesions, and its diagnostic efficacy for risk stratification in patients with intermediate- to high-risk PCa is similar to that of PET/CT and higher than that of bpMRI alone.
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Affiliation(s)
- Yi Nuo
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Aimei Li
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Lulu Yang
- Department of Pathology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Hailin Xue
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Feng Wang
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Liwei Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Peng T, Xiao J, Li L, Pu B, Niu X, Zeng X, Wang Z, Gao C, Li C, Chen L, Yang J. Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis? Int J Comput Assist Radiol Surg 2021; 16:2235-2249. [PMID: 34677748 PMCID: PMC8616865 DOI: 10.1007/s11548-021-02507-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022]
Abstract
Purpose To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation. Methods The dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong’s method. Results Eight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05). Conclusions Each machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02507-w.
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Affiliation(s)
- Tao Peng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - JianMing Xiao
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Lin Li
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - BingJie Pu
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - XiangKe Niu
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China.
| | - XiaoHui Zeng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - ZongYong Wang
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - ChaoBang Gao
- College of Information Science and Technology, Chengdu University, 1 Shiling shang Street, Chengdu, 610106, Sichuan Province, China
| | - Ci Li
- Department of Pathology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Lin Chen
- Department of Urology Surgery, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Jin Yang
- Department of Urology Surgery, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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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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Target Heterogeneity in Oncology: The Best Predictor for Differential Response to Radioligand Therapy in Neuroendocrine Tumors and Prostate Cancer. Cancers (Basel) 2021; 13:cancers13143607. [PMID: 34298822 PMCID: PMC8304541 DOI: 10.3390/cancers13143607] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/04/2021] [Accepted: 07/07/2021] [Indexed: 12/27/2022] Open
Abstract
Simple Summary In the era of precision medicine, novel targets have emerged on the surface of cancer cells, which have been exploited for the purpose of radioligand therapy. However, there have been variations in the way these receptors are expressed, especially in prostate cancers and neuroendocrine tumors. This variable expression of receptors across the grades of cancers led to the concept of ‘target heterogeneity’, which has not just impacted therapeutic decisions but also their outcomes. Radiopharmaceuticals targeting receptors need to be used when there are specific indicators—either clinical, radiological, or at molecular level—warranting their use. In addition, response to these radioligands can be assessed using different techniques, whereby we can prognosticate further outcomes. We shall also discuss, in this review, the conventional as well as novel approaches of detecting heterogeneity in prostate cancers and neuroendocrine tumors. Abstract Tumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of ‘treatment resistance’, to current therapeutic agents. We have focused our discussion on ‘Prostate Cancer’ and ‘Neuroendocrine Tumors’, and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the ‘standard of care’ radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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Liu H, Jiao Z, Han W, Jing B. Identifying the histologic subtypes of non-small cell lung cancer with computed tomography imaging: a comparative study of capsule net, convolutional neural network, and radiomics. Quant Imaging Med Surg 2021; 11:2756-2765. [PMID: 34079739 PMCID: PMC8107316 DOI: 10.21037/qims-20-734] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 11/23/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Discriminating the subtypes of non-small cell lung cancer (NSCLC) based on computed tomography (CT) images is a challenging task for radiologists. Although several machine learning methods such as radiomics, and deep learning methods such as convolutional neural networks (CNNs) have been proposed to explore the problem, large sample sizes are required for effective training, and this may not be easily achieved in single-center datasets. METHODS In this study, an automated subtype recognition model with capsule net (CapsNet) was developed for the subtype discrimination of NSCLC. CapsNet utilizes an activity vector to record the relative spatial relationship of image elements that can subsequently better delineate the global image characteristics. CT images of 72 adenocarcinoma (AC) and 54 squamous cell carcinoma (SCC) patients were retrospectively collected from a single clinical center. The cancer region on the CT image was manually segmented for every subject by an experienced radiologist, and CapsNet, CNN, and four radiomics models were then used to construct the recognition model. RESULTS The study demonstrated that CapsNet achieved the best discriminative performance (accuracy 81.3%, specificity 80.7%, sensitivity 82.2%) although its area under the curve was just marginally better than that of the optimal random forest (RF) based radiomics model. Not surprisingly, the performance of the CNN was only comparable to the other radiomics models. CONCLUSIONS This study demonstrated that CapsNet is a viable potential framework for discriminating the subtypes of NSCLC, and its use could be extended to the recognition of other diseases especially in limited single-center datasets.
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Affiliation(s)
- Han Liu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Zhicheng Jiao
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wenjuan Han
- Department of Radiology, Third Medical Center of PLA General Hospital, Beijing, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
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Yuan J, Xue C, Lo G, Wong OL, Zhou Y, Yu SK, Cheung KY. Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy. Quant Imaging Med Surg 2021; 11:1870-1887. [PMID: 33936971 DOI: 10.21037/qims-20-865] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background MRI pulse sequences and imaging parameters substantially influence the variation of MRI radiomics features, thus impose a critical challenge on MRI radiomics reproducibility and reliability. This study aims to prospectively investigate the impact of various imaging parameters on MRI radiomics features in a 3D T2-weighted (T2W) turbo-spin-echo (TSE) pulse sequence for MR-guided-radiotherapy (MRgRT). Methods An anthropomorphic phantom was scanned using a 3D-T2W-TSE MRgRT sequence at 1.5T under a variety of acquisition imaging parameter changes. T1 and T2 relaxation times of the phantom were also measured. 93 first-order and texture radiomics features in the original and 14 transformed images, yielding 1,395 features in total, were extracted from 10 volumes-of-interest (VOIs). The percentage deviation (d%) of radiomics feature values from the baseline values and intra-class correlation coefficient (ICC) with the baseline were calculated. Robust radiomics features were identified based on the excellent agreement of radiomics feature values with the baseline, i.e., the averaged d% <5% and ICC >0.90 in all VOIs for all imaging parameter variations. Results The radiomics feature values changed considerably but to different degrees with different imaging parameter adjustments, in the ten VOIs. The deviation d% ranged from 0.02% to 321.3%, with a mean of 12.5% averaged for all original features in all ten VOIs. First-order and GLCM features were generally more robust to imaging parameters than other features in the original images. There were also significantly different radiomics feature values (ANOVA, P<0.001) between the original and the transformed images, exhibiting quite different robustness to imaging parameters. 330 out of 1395 features (23.7%) robust to imaging parameters were identified. GLCM and GLSZM features had the most (42.5%, 153/360) and least (3.8%, 9/240) robust features in the original and transformed images, respectively. Conclusions This study helps better understand the quantitative dependence of radiomics feature values on imaging parameters in a 3D-T2W-TSE sequence for MRgRT. Imaging parameter heterogeneity should be considered as a significant source of radiomics variability and uncertainty, which must be well harmonized for reliable clinical use. The identified robust features to imaging parameters are helpful for the pre-selection of radiomics features for reliable radiomics modeling.
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Affiliation(s)
- Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Gladys Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, Hong Kong SAR, China
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Liang L, Zhi X, Sun Y, Li H, Wang J, Xu J, Guo J. A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions. Front Oncol 2021; 11:610785. [PMID: 33738255 PMCID: PMC7962672 DOI: 10.3389/fonc.2021.610785] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 01/25/2021] [Indexed: 12/14/2022] Open
Abstract
Objectives To evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa). Methods A total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve. Results The multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model. Conclusions Clinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.
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Affiliation(s)
- Lei Liang
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Xin Zhi
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Ya Sun
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Huarong Li
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Jiajun Wang
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Jun Guo
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
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Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review. Cancers (Basel) 2021; 13:cancers13030552. [PMID: 33535569 PMCID: PMC7867056 DOI: 10.3390/cancers13030552] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/18/2021] [Accepted: 01/27/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted. Abstract The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.
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Wang L, Li T, Hong J, Zhang M, Ouyang M, Zheng X, Tang K. 18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma. Quant Imaging Med Surg 2021; 11:215-225. [PMID: 33392023 DOI: 10.21037/qims-20-337] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. Methods The preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. Results The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. Conclusions A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.
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Affiliation(s)
- Lili Wang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tiancheng Li
- PET Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junjie Hong
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingyue Zhang
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingli Ouyang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangwu Zheng
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Tang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Ding XF, Luan Y, Wang F, Xu YZ, Guo CH, Zhu LY. The application of a targeted periprostatic nerve block in transperineal template-guided prostate biopsies. Quant Imaging Med Surg 2020; 10:2125-2132. [PMID: 33139992 DOI: 10.21037/qims-20-369] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background Our study aims to evaluate the anesthetic efficacy of multiparametric magnetic resonance imaging/transrectal ultrasound (mpMRI/TRUS) fusion-guided targeted periprostatic nerve block (PNB) for transperineal template-guided prostate biopsy (TTPB). Methods The patients who underwent mpMRI/TRUS fusion-guided prostate biopsy from May 2018 to March 2019 were randomized into two groups using a random number table. The intervention group (n=47) and the control group (n=45) received targeted PNB and traditional PNB, respectively. Visual analog scale (VAS) and visual numeric scale (VNS) scores were used to assess the patients' pain and quantify their satisfaction. Results The total detection rate for prostate cancer was 45.7%, with a comparable positive rate between the intervention group (42.6%) and the control group (48.9%), which meant there was no significant difference between the groups (P=0.542). Patient age, prostate-specific antigen, prostate volume, suspicious lesions on mpMRI, number of cores, operation time, and biopsy time were comparable between the groups. The VAS scores during biopsy were significantly lower in the intervention group than in the control group [2 (1 to 3) vs. 2 (1 to 4), P=0.019]. Conversely, the VNS scores during biopsy were higher in the intervention group [3 (2 to 4) vs. 3 (2 to 3), P=0.015]. There were no significant differences in the pain scores or the satisfaction scores at 30 min after the procedure between the two groups. There were no significant differences between the groups for complications, such as hematuria, urinary retention, infection, hemospermia, and vasovagal reaction (P>0.05). Conclusions Targeted PNB significantly relieved the pain and did not increase the incidence of complications for patients when compared with traditional PNB.
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Affiliation(s)
- Xue-Fei Ding
- Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yang Luan
- Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Fei Wang
- Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yao-Zong Xu
- Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Cheng-Hao Guo
- Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Liang-Yong Zhu
- Clinical Medical College, Yangzhou University, Yangzhou, China
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Delgadillo R, Ford JC, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R. The role of radiomics in prostate cancer radiotherapy. Strahlenther Onkol 2020; 196:900-912. [PMID: 32821953 PMCID: PMC7545508 DOI: 10.1007/s00066-020-01679-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/07/2020] [Indexed: 12/24/2022]
Abstract
"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.
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Affiliation(s)
- Rodrigo Delgadillo
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA.
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Impact of radiomics on prostate cancer detection: a systematic review of clinical applications. Curr Opin Urol 2020; 30:754-781. [DOI: 10.1097/mou.0000000000000822] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Liu L, Yi X, Lu C, Qi L, Zhang Y, Li M, Xiao Q, Wang C, Zhang L, Pang Y, Wang Y, Guan X. Applications of radiomics in genitourinary tumors. Am J Cancer Res 2020; 10:2293-2308. [PMID: 32905456 PMCID: PMC7471369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 07/02/2020] [Indexed: 06/11/2023] Open
Abstract
Genitourinary tumors are heterogeneous groups of tumors with high morbidity and mortality rates. Confronted with existing problems in the management of genitourinary tumors, a personalized imaging method called radiomics shows great potential in areas including detection, grading, and treatment response assessment. Radiomics is characterized by extraction of quantitative imaging features which are not visible to the naked eye from conventional imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT), followed by data analysis and model building. It outperforms other invasive methods in terms of non-invasiveness, low cost and high efficiency. Recently, a number of studies have evaluated the application of radiomics in patients with genitourinary tumors with promising data. The combination of radiomics and clinical/laboratory factors provides added value in many studies. Despite this, there are limitations and challenges to be overcome before a more extensive clinical application in the future. In this article, we will introduce the concept, significance and workflow of radiomics, review their current applications in patients with genitourinary tumors and discuss limitations and future directions of radiomics. It would help multidisciplinary team involved in the treatment of patients with genitourinary tumors to achieve a better understanding of the results of radiomics study toward a personalized medicine.
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Affiliation(s)
- Longfei Liu
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Xiaoping Yi
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
- Department of Radiology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Can Lu
- Department of Nephrology, The Second Xiangya Hospital of Central South UniversityChangsha 410000, Hunan, P. R. China
| | - Lin Qi
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Youming Zhang
- Department of Radiology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Minghao Li
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Qiao Xiao
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Cikui Wang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Liang Zhang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Yingxian Pang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Yong Wang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Xiao Guan
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
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