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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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Tang J, Zheng X, Wang X, Mao Q, Xie L, Wang R. Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: A promising approach for early diagnosis. Technol Health Care 2024; 32:125-133. [PMID: 38759043 PMCID: PMC11191472 DOI: 10.3233/thc-248011] [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: 05/19/2024]
Abstract
BACKGROUND Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate. OBJECTIVE In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images. METHODS We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected. RESULTS Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73. CONCLUSION The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.
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Affiliation(s)
- Jianer Tang
- Department of Urology, First Affiliated Hospital, Medical College of Zhejiang University, Hangzhou, Zhejiang, China
- Department of Urology, First Affiliated Hospital of Huzhou Teachers College, Huzhou, Zhejiang, China
| | - Xiangyi Zheng
- Department of Urology, First Affiliated Hospital, Medical College of Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiao Wang
- Department of Urology, First Affiliated Hospital, Medical College of Zhejiang University, Hangzhou, Zhejiang, China
| | - Qiqi Mao
- Department of Urology, First Affiliated Hospital, Medical College of Zhejiang University, Hangzhou, Zhejiang, China
| | - Liping Xie
- Department of Urology, First Affiliated Hospital, Medical College of Zhejiang University, Hangzhou, Zhejiang, China
| | - Rongjiang Wang
- Department of Urology, First Affiliated Hospital of Huzhou Teachers College, Huzhou, Zhejiang, China
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Li J, Wang K, Li S, Wu P, Wang X, He Y, Tang W. Clinical study of multifactorial diagnosis in prostate biopsy. Prostate 2023; 83:1494-1503. [PMID: 37545333 DOI: 10.1002/pros.24608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/14/2023] [Accepted: 07/24/2023] [Indexed: 08/08/2023]
Abstract
PURPOSE To study the feasibility of using an artificial intelligence (AI) algorithm for the diagnosis of clinically significant prostate cancer (csPCa) on multiparametric MRI (mpMRI) in combination with conventional clinical information. METHODS A retrospective study cohort with 505 patients was collected, with complete information on age (≤60, 60-80, and >80 years), PSA (≤4, 4-10, and >10 ng/dL), and pathology results. The patients with ISUP group >2 were classified as csPCa, and the patients with ISUP = 1 or no evidence of prostate cancer were classified as non-csPCa. The diagnosis of mpMRI was made by experienced radiologists following the prostate imaging reporting and data system (PIRADS ≤ 2, PIRADS = 3, and PIRADS > 3). The mpMRI images were processed by a homemade AI algorithm, and the AI results were obtained as positive or negative for csPCa. Two logistic regression models were fitted, with pathological findings as the dependent variable, that is, a conventional model and an AI model. The conventional model used age, PSA, and PIRADS as the independent variables. The AI model took the AI result and the abovementioned clinical information as the independent variables. The predicted probability of the patients from the conventional model and the AI model were used to test the prediction efficacy of the models. The DeLong test was performed to compare differences in the area under the receiver operating characteristic (ROC) area under the curve (AUC) between the conventional model and the AI model. RESULTS In total, 505 patients were included in the study; 280 were diagnosed with csPCa, and 225 were non-csPCa. The median age was 72.0 (67.0, 76.0) years, with a median PSA value of 13.0 (7.46, 27.5) ng/dL. Statically significant differences were found in age, PSA, PIRADS score and AI results between the csPCa and non-csPCa groups (all p < 0.001). In the multivariable regression models, all the variables were independently associated with csPCa. The conventional model (R2 = 0.361) and the AI model (R2 = 0.474) were compared with analysis of variance (ANOVA) and showed statistically significant differences (χ2 = 63.695, p < 0.001). The AUC of the ROC curve for the conventional model was 0.782 (95% confidence interval [CI]: 0.742-0.823), which was less than the AUC of the AI model with statistical significance (0.849 [95% CI: 0.815-0.883], p < 0.001). CONCLUSION In combination with routine clinical information, such as age, PSA, and PIRADS category, adding information from the AI algorithm based on mpMRI could improve the diagnosis of csPCa.
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Affiliation(s)
- Jialei Li
- Zhejiang Chinese Medical University, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Song Li
- Zhejiang Chinese Medical University, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Yi He
- The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Wenrui Tang
- The Affiliated Hospital of Jiaxing University, Jiaxing, China
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Gibala S, Obuchowicz R, Lasek J, Schneider Z, Piorkowski A, Pociask E, Nurzynska K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. J Clin Med 2023; 12:jcm12082836. [PMID: 37109173 PMCID: PMC10146387 DOI: 10.3390/jcm12082836] [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: 03/17/2023] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. METHODS Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. RESULTS Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. CONCLUSIONS There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.
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Affiliation(s)
- Sebastian Gibala
- Urology Department, Ultragen Medical Center, 31-572 Krakow, Poland
| | - Rafal Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
| | - Julia Lasek
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Zofia Schneider
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Adam Piorkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Elżbieta Pociask
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Karolina Nurzynska
- Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland
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Xue P, Si M, Qin D, Wei B, Seery S, Ye Z, Chen M, Wang S, Song C, Zhang B, Ding M, Zhang W, Bai A, Yan H, Dang L, Zhao Y, Rezhake R, Zhang S, Qiao Y, Qu Y, Jiang Y. Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis. J Med Internet Res 2023; 25:e43832. [PMID: 36862499 PMCID: PMC10020907 DOI: 10.2196/43832] [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: 10/26/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.
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Affiliation(s)
- Peng Xue
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongxu Qin
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bingrui Wei
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | - Zichen Ye
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyang Chen
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sumeng Wang
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cheng Song
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Ding
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenling Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Anying Bai
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijiao Yan
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Le Dang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuqian Zhao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science & Technology of China, Sichuan, China
| | - Remila Rezhake
- Affiliated Cancer Hospital, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University, Xinjiang, China
| | - Shaokai Zhang
- Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Henan, China
| | - Youlin Qiao
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yimin Qu
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Bao J, Hou Y, Qin L, Zhi R, Wang XM, Shi HB, Sun HZ, Hu CH, Zhang YD. High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade. Br J Cancer 2023; 128:1267-1277. [PMID: 36646808 PMCID: PMC10050457 DOI: 10.1038/s41416-022-02134-5] [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: 08/21/2022] [Revised: 12/11/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). METHODS This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. RESULTS PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction. CONCLUSIONS PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.
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Affiliation(s)
- Jie Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188N, Shizi Road, 215006, Suzhou, Jiangsu, China
| | - Ying Hou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Lang Qin
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Rui Zhi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Xi-Ming Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188N, Shizi Road, 215006, Suzhou, Jiangsu, China
| | - Hai-Bin Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Hong-Zan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Chun-Hong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188N, Shizi Road, 215006, Suzhou, Jiangsu, China.
| | - Yu-Dong Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China.
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Back Propagation Neural Network-Based Magnetic Resonance Imaging Image Features in Treating Intestinal Obstruction in Digestive Tract Diseases with Chengqi Decoction. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2021:1667024. [PMID: 35024009 PMCID: PMC8719996 DOI: 10.1155/2021/1667024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/31/2021] [Accepted: 11/10/2021] [Indexed: 11/17/2022]
Abstract
This study was to explore the adoption effect of magnetic resonance imaging (MRI) image features based on back propagation neural network (BPNN) in evaluating the curative effect of Chengqi Decoction (CD) for intestinal obstruction (ileus), so as to evaluate the clinical adoption value of this algorithm. Ninety patients with ileus were recruited, and the patients were treated with CD and underwent MRI scans of the lower abdomen. A BPNN model was fabricated and applied to segment the MRI images of patients and identify the lesion. As a result, when the overlap step was 16 and the block size was 32 × 32, the running time of the BPNN algorithm was the shortest. The segmentation accuracy was the highest if there were two hidden layer (HL) nodes, reaching 97.3%. The recognition rates of small intestinal stromal tumor (SIST), colon cancer, adhesive ileus, and volvulus of MRI images segmented by the algorithm were 91.5%, 88.33%, 90.3%, and 88.9%, respectively, which were greatly superior to those of manual interpretation (P < 0.05). After the intervention of CD, the percentages of patients with ileus that were cured, markedly effective, effective, and ineffective were 65.38%, 23.16%, 5.38%, and 6.08%, respectively. The cure rate after intervention of CD (65.38%) was much higher in contrast to that before intervention (13.25%) (P < 0.05). In short, CD showed a good therapeutic effect on ileus and can effectively improve the prognosis of patients. In addition, MRI images based on BPNN showed a good diagnostic effect on ileus, and it was worth applying to clinical diagnosis.
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Singh R, Alsayadi YMMA, Singh V, Chawla P, Rawal RK. Prospects of Treating Prostate Cancer through Apalutamide: A Mini-Review. Anticancer Agents Med Chem 2021; 22:1056-1067. [PMID: 34431470 DOI: 10.2174/1871520621666210824113736] [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: 02/10/2021] [Revised: 07/07/2021] [Accepted: 07/12/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Prostate cancer is considered the second most diagnosed cancer, and one of the most common causes of death from cancer in men. Apalutamide is an effective, safe, and well-tolerated agent used for the treatment of men with non-metastatic castration-resistant prostate cancer (nmCRPC) and metastatic hormone-naive prostate cancer (mHNPC). Androgen receptor signaling is a leading factor that drives these prostate tumors. USFDA has approved apalutamide on 14 February 2018 as an agent that targets androgen receptor signaling through inhibition causing significant improvement in metastasis-free survival in patients with prostate cancer. <P> Objective: In this review, various aspects related to apalutamide have been summarized which involve the mechanism of action, chemistry, synthesis, pharmacokinetics, pharmacodynamics, adverse reactions, and safety parameters. <P> Methods: The literature was thoroughly searched in the relevant databases to identify studies published in this field during recent years. Special attention has been given to apalutamide clinical trials phases and its promising future as one of the first-line agents for the treatment of patients with advanced prostate cancer. <P> Results: Ongoing trials are progressing for apalutamide monotherapy and also for its combinations in other disease settings. The expected results of such trials will shape the future scenario of prostate cancer therapy. <P> Conclusion: This review article has highlighted different aspects of Apalutamide like its mechanism of action, adverse effects, pharmacokinetics, pharmacodynamics, clinical trials among others. The contents of this article should make an excellent read for prospective researchers in this field.
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Affiliation(s)
- Ranapartap Singh
- Department of Pharmaceutical Chemistry & Analysis, ISF College of Pharmacy, GT Road, Moga-142001, Punjab. India
| | - Yunes M M A Alsayadi
- Department of Pharmaceutical Chemistry & Analysis, ISF College of Pharmacy, GT Road, Moga-142001, Punjab. India
| | - VikramJeet Singh
- Department of Pharmaceutical Chemistry & Analysis, ISF College of Pharmacy, GT Road, Moga-142001, Punjab. India
| | - Pooja Chawla
- Department of Pharmaceutical Chemistry & Analysis, ISF College of Pharmacy, GT Road, Moga-142001, Punjab. India
| | - Ravindra Kumar Rawal
- Department of Chemistry, Maharishi Markandeshwar (Deemed to be University), Mullana-133207, Haryana. India
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Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11060959. [PMID: 34073627 PMCID: PMC8229869 DOI: 10.3390/diagnostics11060959] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022] Open
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
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Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021; 23:e22394. [PMID: 33792552 PMCID: PMC8050752 DOI: 10.2196/22394] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 11/26/2020] [Accepted: 01/17/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. OBJECTIVE This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. RESULTS In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. CONCLUSIONS The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.
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Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering. Cancers (Basel) 2021; 13:cancers13071524. [PMID: 33810251 PMCID: PMC8036750 DOI: 10.3390/cancers13071524] [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: 02/01/2021] [Revised: 03/09/2021] [Accepted: 03/23/2021] [Indexed: 12/27/2022] Open
Abstract
Simple Summary Artificial intelligence techniques were used for the detection of prostate cancer through tissue feature engineering. A radiomic method was used to extract the important features or information from histopathology tissue images to perform binary classification (i.e., benign vs. malignant). This method can identify a histological pattern that is invisible to the human eye, which helps researchers to predict and detect prostate cancer. We used different performance metrics to evaluate the results of the classification. In the future, it is expected that a method like radiomic will provide a consistent contribution to analyze histopathology tissue images and differentiate between cancerous and noncancerous tumors. Abstract The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.
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Xing X, Zhao X, Wei H, Li Y. Diagnostic accuracy of different computer-aided diagnostic systems for prostate cancer based on magnetic resonance imaging: A systematic review with diagnostic meta-analysis. Medicine (Baltimore) 2021; 100:e23817. [PMID: 33545946 PMCID: PMC7837946 DOI: 10.1097/md.0000000000023817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 03/07/2020] [Accepted: 11/19/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Computer-aided detection (CAD) system for accurate and automated prostate cancer (PCa) diagnosis have been developed, however, the diagnostic test accuracy of different CAD systems is still controversial. This systematic review aimed to assess the diagnostic accuracy of CAD systems based on magnetic resonance imaging for PCa. METHODS Cochrane library, PubMed, EMBASE and China Biology Medicine disc were systematically searched until March 2019 for original diagnostic studies. Two independent reviewers selected studies on CAD based on magnetic resonance imaging diagnosis of PCa and extracted the requisite data. Pooled sensitivity, specificity, and the area under the summary receiver operating characteristic curve were calculated to estimate the diagnostic accuracy of CAD system. RESULTS Fifteen studies involving 1945 patients were included in our analysis. The diagnostic meta-analysis showed that overall sensitivity of CAD system ranged from 0.47 to 1.00 and, specificity from 0.47 to 0.89. The pooled sensitivity of CAD system was 0.87 (95% CI: 0.76-0.94), pooled specificity 0.76 (95% CI: 0.62-0.85), and the area under curve (AUC) 0.89 (95% CI: 0.86-0.91). Subgroup analysis showed that the support vector machines produced the best AUC among the CAD classifiers, with sensitivity ranging from 0.87 to 0.92, and specificity from 0.47 to 0.95. Among different zones of prostate, CAD system produced the best AUC in the transitional zone than the peripheral zone and central gland; sensitivity ranged from 0.89 to 1.00, and specificity from 0.38 to 0.85. CONCLUSIONS CAD system can help improve the diagnostic accuracy of PCa especially using the support vector machines classifier. Whether the performance of the CAD system depends on the specific locations of the prostate needs further investigation.
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Affiliation(s)
- Xiping Xing
- Affiliated hospital of Gansu University of Chinese Medicine
| | - Xinke Zhao
- Affiliated hospital of Gansu University of Chinese Medicine
| | - Huiping Wei
- Affiliated hospital of Gansu University of Chinese Medicine
| | - Yingdong Li
- Gansu University of Traditional Chinese Medicine, Lanzhou, China
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The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.
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McGarry SD, Bukowy JD, Iczkowski KA, Lowman AK, Brehler M, Bobholz S, Nencka A, Barrington A, Jacobsohn K, Unteriner J, Duvnjak P, Griffin M, Hohenwalter M, Keuter T, Huang W, Antic T, Paner G, Palangmonthip W, Banerjee A, LaViolette PS. Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer. J Med Imaging (Bellingham) 2020; 7:054501. [PMID: 32923510 PMCID: PMC7479263 DOI: 10.1117/1.jmi.7.5.054501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/20/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose: Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. Approach: Clinical imaging and postsurgical tissue from 48 recruited prospective patients were used in our study. Tissue was sliced to match the MRI orientation and whole-mount slides were stained and digitized. Data from 28 patients ( n = 33 slides) were sent to five pathologists to be annotated. Slides from the remaining 20 patients ( n = 123 slides) were annotated by one of the five pathologists. Interpathologist variability was measured using Krippendorff's alpha. Pathologist-specific radiopathomic mapping models were trained using a partial least-squares regression using MRI values to predict epithelium density, a known marker for disease severity. An analysis of variance characterized intermodel means difference in epithelium density. A consensus model was created and evaluated using a receiver operator characteristic classifying high grade versus low grade and benign, and was statistically compared to apparent diffusion coefficient (ADC). Results: Interobserver variability ranged from low to acceptable agreement (0.31 to 0.69). There was a statistically significant difference in mean predicted epithelium density values ( p < 0.001 ) between the five models. The consensus model outperformed ADC (areas under the curve = 0.80 and 0.71, respectively, p < 0.05 ). Conclusion: We demonstrate that radiopathomic maps of epithelium density are sensitive to the pathologist annotating the dataset; however, it is unclear if these differences are clinically significant. The consensus model produced the best maps, matched the performance of the best individual model, and outperformed ADC.
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Affiliation(s)
- Sean D McGarry
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - John D Bukowy
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth A Iczkowski
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States
| | - Allison K Lowman
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Michael Brehler
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Samuel Bobholz
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - Andrew Nencka
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Alex Barrington
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth Jacobsohn
- Medical College of Wisconsin, Department of Urological Surgery, Milwaukee, Wisconsin, United States
| | - Jackson Unteriner
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Petar Duvnjak
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Michael Griffin
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Mark Hohenwalter
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Tucker Keuter
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - Wei Huang
- University of Wisconsin-Madison, Department of Pathology, Madison, Wisconsin, United States
| | - Tatjana Antic
- University of Chicago, Department of Pathology, Chicago, Illinois, United States
| | - Gladell Paner
- University of Chicago, Department of Pathology, Chicago, Illinois, United States
| | - Watchareepohn Palangmonthip
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States.,Chiang Mai University, Department of Pathology, Faculty of Medicine, Chiang Mai, Thailand
| | - Anjishnu Banerjee
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - Peter S LaViolette
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Biomedical Engineering, Milwaukee, Wisconsin, United States
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Nelson CR, Ekberg J, Fridell K. Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence. ACTA ACUST UNITED AC 2020. [DOI: 10.2174/1874061802006010001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer.
Aim:
The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening.
Methods:
This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results.
Results:
CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist.
Conclusion:
When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.
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Zhu L, Gao G, Liu Y, Han C, Liu J, Zhang X, Wang X. Feasibility of integrating computer-aided diagnosis with structured reports of prostate multiparametric MRI. Clin Imaging 2019; 60:123-130. [PMID: 31874336 DOI: 10.1016/j.clinimag.2019.12.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 12/02/2019] [Accepted: 12/11/2019] [Indexed: 01/05/2023]
Abstract
OBJECTIVES To evaluate the feasibility of integrating computer-aided diagnosis (CAD) with structured reports of prostate multiparametric MRI (mpMRI). METHODS This retrospective study enrolled 153 patients who underwent prostate mpMRI for the purpose of targeted biopsy; patients were divided into a group with clinically significant prostate cancer (csPCa, Gleason score ≥ 3 + 4, n = 89) and a group with non-csPCa (n = 64). Ten inexperienced radiologists retrospectively evaluated these cases (single reader per case) twice using structured reports, and they were blinded to the pathologic results. Initially, the readers interpreted mpMRI without CAD. Six weeks later, they evaluated the same cases again with CAD assistance. At each time of image interpretation, lesions detected by the readers were marked on the prostate vector map in structured reports, and a PI-RADS score was given to each lesion. Diagnostic efficacy and reading time were evaluated for the two reading sessions. RESULTS With the assistance of CAD, the overall diagnostic efficacy was improved, i.e., the AUC increased from 0.83 to 0.89 (p = 0.018). Specifically, per-patient sensitivity (84.3% vs. 93.3%) and per-lesion sensitivity (76.7% vs. 88.8%) were significantly improved (all p < 0.05). Per-patient specificity with CAD (65.6%) was higher than that without CAD (56.3%), but statistical significance was not reached (p = 0.238). The reading time for each case decreased from 10.9 min to 7.8 min (p < 0.001). CONCLUSIONS It is feasible to integrate CAD with structured reports of prostate mpMRI. This reading paradigm can improve the diagnostic sensitivity of csPCa detection and reduce reading time among inexperienced radiologists.
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Affiliation(s)
- Lina Zhu
- Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China
| | - Ge Gao
- Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China
| | - Yi Liu
- Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China
| | - Chao Han
- Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China
| | - Jing Liu
- Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
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Zhu Y, Wei R, Gao G, Ding L, Zhang X, Wang X, Zhang J. Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. J Magn Reson Imaging 2018; 49:1149-1156. [PMID: 30350434 DOI: 10.1002/jmri.26337] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/29/2018] [Accepted: 08/31/2018] [Indexed: 12/17/2022] Open
Affiliation(s)
- Yi Zhu
- Academy for Advanced Interdisciplinary StudiesPeking University Beijing P.R. China
| | - Rong Wei
- Academy for Advanced Interdisciplinary StudiesPeking University Beijing P.R. China
| | - Ge Gao
- Department of RadiologyPeking University First Hospital Beijing P.R. China
| | - Lian Ding
- Academy for Advanced Interdisciplinary StudiesPeking University Beijing P.R. China
| | - Xiaodong Zhang
- Department of RadiologyPeking University First Hospital Beijing P.R. China
| | - Xiaoying Wang
- Academy for Advanced Interdisciplinary StudiesPeking University Beijing P.R. China
- Department of RadiologyPeking University First Hospital Beijing P.R. China
| | - Jue Zhang
- Academy for Advanced Interdisciplinary StudiesPeking University Beijing P.R. China
- College of EngineeringPeking University Beijing P.R. China
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An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2017-0629] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Abstract
Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing.
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Abstract
Multi-parametric magnetic resonance imaging (mp-MRI) has an increasingly important role in the diagnosis of prostate cancer. Due to the large amount of data and variations in mp-MRI, tumor detection can be affected by multiple factors, such as the observer's clinical experience, image quality, and appearance of the lesions. In order to improve the quantitative assessment of the disease and reduce the reporting time, various computer-aided diagnosis (CAD) systems have been designed to help radiologists identify lesions. This manuscript presents an overview of the literature regarding prostate CAD using mp-MRI, while focusing on the studies of the most recent five years. Current prostate CAD technologies and their utilization are discussed in this review.
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Jiang T, Xie L, Lou X, Li D, Chen Z, Xiao H, Ma L. T2 relaxation time measurements in the brains of scalded rats. SCIENCE CHINA-LIFE SCIENCES 2017; 60:5-10. [PMID: 28078505 DOI: 10.1007/s11427-016-0382-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 11/07/2016] [Indexed: 12/23/2022]
Abstract
This study aimed to evaluate the T2 relaxation time of the brain in severely scalded rats using a magnetic resonance (MR) T2 mapping sequence, and to investigate the correlation between T2 relaxation time and plasma glucose level. Twenty-eight Wistar rats were randomly divided into the scalded group (n=21) and control group (n=7). Magnetic resonance scans were performed with T1WI, T2WI, and T2-mapping sequences in the scalded group; the scans were performed 1 day prior to scalding and 1, 3, 5, and 7 days post-scalding; in addition, identical MR scans were performed in the control group at the same time points. T2-maps were generated and T2 relaxation times were acquired from the following brain regions: the hippocampus, thalamus, caudate-putamen, and cerebrum. Pathological changes of the hippocampus were observed. The plasma glucose level of each rat was measured before each MR scan, and a correlation analysis was performed between T2 relaxation time and plasma glucose level. We found that conventional T1WI and T2WI did not reveal any abnormal signals or morphological changes in the hippocampus, thalamus, caudate-putamen, or cerebrum post-scalding. Both the T2 relaxation times of the selected brain regions and plasma glucose levels increased 1, 3, and 5 days post-scalding, and returned to normal levels 7 days post-scalding. The most marked increase of T2 relaxation time was found in the hippocampus; similar changes were also revealed in the thalamus, caudate-putamen, and cerebrum. No correlation was found between T2 relaxation time and plasma glucose level in scalded rats. Pathological observation of the hippocampus showed edema 1, 3, and 5 days post-scalding, with recovery to normal findings at 7 days post-scalding. Thus, we concluded that T2 mapping is a sensitive method for detecting and monitoring scald injury in the rat brain. As the hippocampus is the main region for modulating a stress reaction, it showed significantly increased water content along with an increased plasma glucose level post-scalding.
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Affiliation(s)
- Tao Jiang
- Department of Radiology, PLA General Hospital, Beijing, 100853, China
- Department of Radiology, PLA 401 Hospital, Qingdao, 266071, China
| | - Liqi Xie
- Department of Radiology, PLA 401 Hospital, Qingdao, 266071, China
| | - Xin Lou
- Department of Radiology, PLA General Hospital, Beijing, 100853, China
| | - Dawei Li
- Department of Burn and Plastic Surgery, PLA 304 Hospital, Beijing, 100048, China
| | - Zhiye Chen
- Department of Radiology, PLA General Hospital, Beijing, 100853, China
| | - Huafeng Xiao
- Department of Radiology, PLA 302 Hospital, Beijing, 100039, China
| | - Lin Ma
- Department of Radiology, PLA General Hospital, Beijing, 100853, China.
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Gao G, Wang C, Zhang X, Hu J, Yang X, Wang H, Zhang J, Wang X. Quantitative analysis of diffusion-weighted magnetic resonance images: differentiation between prostate cancer and normal tissue based on a computer-aided diagnosis system. SCIENCE CHINA-LIFE SCIENCES 2017; 60:37-43. [DOI: 10.1007/s11427-016-0389-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 09/28/2016] [Indexed: 12/24/2022]
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22
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Liu L, Tian Z, Zhang Z, Fei B. Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications. Acad Radiol 2016; 23:1024-46. [PMID: 27133005 PMCID: PMC5355004 DOI: 10.1016/j.acra.2016.03.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Revised: 03/18/2016] [Accepted: 03/21/2016] [Indexed: 01/10/2023]
Abstract
One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
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Affiliation(s)
- Lizhi Liu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329
| | - Zhenfeng Zhang
- Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1841 Clifton Road NE, Atlanta, Georgia 30329; Winship Cancer Institute of Emory University, 1841 Clifton Road NE, Atlanta, Georgia 30329.
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How are we going to train a generation of radiologists (and urologists) to read prostate MRI? Curr Opin Urol 2016; 25:522-35. [PMID: 26375060 DOI: 10.1097/mou.0000000000000217] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE OF REVIEW Multiparametric MRI has gained tremendous importance in the daily practice for patients at risk or diagnosed with prostate cancer. Interpretation of multiparametric-MRI is a complex task, supposedly restricted to experienced radiologists. The purpose of this review is to analyze fundamentals of multiparametric-MRI interpretation and to describe how multiparametric-MRI training could be organized. RECENT FINDINGS Recently, professional guidelines have been published to provide technical and interpretation frameworks and harmonize multiparametric-MRI practice, but the question of physicians training in prostate multiparametric-MRI reading is still pending. What kind of education, practice, and training makes a radiologist able to reliably interpret a prostate multiparametric-MRI? How can findings be reported to be easily understood? How much experience is needed? How can we train urologists and other physicians to review the examinations they request? Is double-reading necessary? SUMMARY An institutional-based competency certification process for prostate multiparametric-MRI interpretation may encourage nonspecialized radiologists to qualify for prostate imaging in a standardized and reproducible way, exactly as urologists need it.
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