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Jin L, Yu Z, Gao F, Li M. T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study. Insights Imaging 2024; 15:111. [PMID: 38713377 PMCID: PMC11076444 DOI: 10.1186/s13244-024-01682-z] [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: 03/13/2024] [Accepted: 03/23/2024] [Indexed: 05/08/2024] Open
Abstract
OBJECTIVES To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. METHODS Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. RESULTS For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. CONCLUSION Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. CRITICAL RELEVANCE STATEMENT Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. KEY POINTS Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.
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Affiliation(s)
- Liang Jin
- Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai, 200040, China
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Zhuo Yu
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Feng Gao
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Ming Li
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China.
- Institute of Functional and Molecular Medical Imaging, Shanghai, 200040, China.
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Huang TL, Lu NH, Huang YH, Twan WH, Yeh LR, Liu KY, Chen TB. Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images. Sci Rep 2023; 13:21849. [PMID: 38071254 PMCID: PMC10710441 DOI: 10.1038/s41598-023-49159-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.
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Affiliation(s)
- Te-Li Huang
- Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung, 81362, Taiwan
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan.
- Department of Pharmacy, Tajen University, No.20, Weixin Rd., Yanpu Township, Pingtung, 90741, Taiwan.
- Department of Radiology, E-DA Hospital, I-Shou University, No.1, Yida Rd., Jiao-Su Village, Yan-Chao District, Kaohsiung, 82445, Taiwan.
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan
| | - Wen-Hung Twan
- Department of Life Sciences, National Taitung University, No.369, Sec. 2, University Rd., Taitung, 95092, Taiwan
| | - Li-Ren Yeh
- Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, No.1, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Hospital, I-Shou University, No.1, Yida Rd., Jiao-Su Village, Yan-Chao District, Kaohsiung, 82445, Taiwan
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan.
- Institute of Statistics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu, 30010, Taiwan.
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Krishnan G, Singh S, Pathania M, Gosavi S, Abhishek S, Parchani A, Dhar M. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023; 6:1227091. [PMID: 37705603 PMCID: PMC10497111 DOI: 10.3389/frai.2023.1227091] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.
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Affiliation(s)
- Gokul Krishnan
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shiana Singh
- Department of Emergency Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Monika Pathania
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Siddharth Gosavi
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shuchi Abhishek
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Ashwin Parchani
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Minakshi Dhar
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
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Froń A, Semianiuk A, Lazuk U, Ptaszkowski K, Siennicka A, Lemiński A, Krajewski W, Szydełko T, Małkiewicz B. Artificial Intelligence in Urooncology: What We Have and What We Expect. Cancers (Basel) 2023; 15:4282. [PMID: 37686558 PMCID: PMC10486651 DOI: 10.3390/cancers15174282] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
INTRODUCTION Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. METHODOLOGY We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. RESULTS Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. CONCLUSIONS AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.
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Affiliation(s)
- Anita Froń
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Alina Semianiuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Uladzimir Lazuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Kuba Ptaszkowski
- Department of Physiotherapy, Wroclaw Medical University, 50-368 Wroclaw, Poland;
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-556 Wroclaw, Poland;
| | - Artur Lemiński
- Department of Urology and Urological Oncology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Wojciech Krajewski
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Tomasz Szydełko
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Bartosz Małkiewicz
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
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Sun Z, Wang K, Kong Z, Xing Z, Chen Y, Luo N, Yu Y, Song B, Wu P, Wang X, Zhang X, Wang X. A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI. Insights Imaging 2023; 14:72. [PMID: 37121983 PMCID: PMC10149551 DOI: 10.1186/s13244-023-01421-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 04/05/2023] [Indexed: 05/02/2023] Open
Abstract
BACKGROUND AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. MATERIALS AND METHODS In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. RESULTS On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001). CONCLUSIONS AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence. CLINICAL RELEVANCE STATEMENT This study involves the process of data collection, randomization and crossover reading procedure.
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Affiliation(s)
- Zhaonan Sun
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Zixuan Kong
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Zhangli Xing
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ning Luo
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yang Yu
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, 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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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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Yu R, Jiang KW, Bao J, Hou Y, Yi Y, Wu D, Song Y, Hu CH, Yang G, Zhang YD. PI-RADS AI: introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI. Br J Cancer 2023; 128:1019-1029. [PMID: 36599915 PMCID: PMC10006083 DOI: 10.1038/s41416-022-02137-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND This study aims to develop and validate an artificial intelligence (AI)-aided Prostate Imaging Reporting and Data System (PI-RADSAI) for prostate cancer (PCa) diagnosis based on MRI. METHODS The deidentified MRI data of 1540 biopsy-naïve patients were collected from four centres. PI-RADSAI is a two-stage, human-in-the-loop AI capable of emulating the diagnostic acumen of subspecialists for PCa on MRI. The first stage uses a UNet-Seg model to detect and segment biopsy-candidate prostate lesions, whereas the second stage leverages UNet-Seg segmentation is trained specifically with subspecialist' knowledge-guided 3D-Resnet to achieve an automatic AI-aided diagnosis for PCa. RESULTS In the independent test set, UNet-Seg identified 87.2% (628/720) of target lesions, with a Dice score of 44.9% (range, 22.8-60.2%) in segmenting lesion contours. In the ablation experiment, the model trained with the data from three centres was superior (kappa coefficient, 0.716 vs. 0.531) to that trained with single-centre data. In the internal and external tests, the triple-centre PI-RADSAI model achieved an overall agreement of 58.4% (188/322) and 60.1% (92/153) with a referential subspecialist in scoring target lesions; when one-point margin of error was permissible, the agreement rose to 91.3% (294/322) and 97.3% (149/153), respectively. In the paired test, PI-RADSAI outperformed 5/11 (45.5%) and matched the performance of 3/11 (27.3%) general radiologists in achieving a clinically significant PCa diagnosis (area under the curve, internal test, 0.801 vs. 0.770, p < 0.01; external test, 0.833 vs. 0.867, p = 0.309). CONCLUSIONS Our closed-loop PI-RADSAI outperforms or matches the performance of more than 70% of general readers in the MRI assessment of PCa. This system might provide an alternative to radiologists and offer diagnostic benefits to clinical practice, especially where subspecialist expertise is unavailable.
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Affiliation(s)
- Ruiqi Yu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663N. Zhongshan Rd., 20062, Shanghai, China
| | - Ke-Wen Jiang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300N, Guangzhou Rd., 210029, Nanjing, Jiangsu Province, China
| | - Jie Bao
- Department of Radiology, the First Affiliated Hospital of Soochow University, 899N, Pinghai Rd., 215006, Suzhou, China
| | - Ying Hou
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300N, Guangzhou Rd., 210029, Nanjing, Jiangsu Province, China
| | - Yinqiao Yi
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663N. Zhongshan Rd., 20062, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663N. Zhongshan Rd., 20062, Shanghai, China
| | - Yang Song
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663N. Zhongshan Rd., 20062, Shanghai, China
| | - Chun-Hong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, 899N, Pinghai Rd., 215006, Suzhou, China.
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663N. Zhongshan Rd., 20062, Shanghai, China.
| | - Yu-Dong Zhang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300N, Guangzhou Rd., 210029, Nanjing, Jiangsu Province, China.
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Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, Turkbey B. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. J Am Coll Radiol 2023; 20:134-145. [PMID: 35922018 PMCID: PMC9887098 DOI: 10.1016/j.jacr.2022.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research. MATERIALS AND METHODS This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons. RESULTS In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation. CONCLUSION A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.
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Affiliation(s)
- Mason J Belue
- Medical Research Scholars Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan S Lay
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Intramural Research Training Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Tim E Phelps
- Postdoctoral Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Artificial Intelligence Resource, Chief of Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Senior Clinician/Director, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
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Jiang KW, Song Y, Hou Y, Zhi R, Zhang J, Bao ML, Li H, Yan X, Xi W, Zhang CX, Yao YF, Yang G, Zhang YD. Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study. J Magn Reson Imaging 2022; 57:1352-1364. [PMID: 36222324 DOI: 10.1002/jmri.28427] [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: 05/24/2022] [Revised: 08/28/2022] [Accepted: 08/29/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND The high level of expertise required for accurate interpretation of prostate MRI. PURPOSE To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI. STUDY TYPE Retrospective. SUBJECTS One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test). FIELD STRENGTH/SEQUENCE 3.0T/scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and apparent diffusion coefficient map. ASSESSMENT Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology. STATISTICAL TESTS Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I2 analysis. RESULTS In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I2 = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05). DATA CONCLUSION Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ke-Wen Jiang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.,AI Imaging Lab, Medical Imaging College, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
| | - Yang Song
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Ying Hou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.,AI Imaging Lab, Medical Imaging College, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
| | - Rui Zhi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.,AI Imaging Lab, Medical Imaging College, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
| | - Jing Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.,AI Imaging Lab, Medical Imaging College, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
| | - Mei-Ling Bao
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
| | - Hai Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Shanghai, People's Republic of China
| | - Wei Xi
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Cheng-Xiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Ye-Feng Yao
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China
| | - Yu-Dong Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.,AI Imaging Lab, Medical Imaging College, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
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10
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Zhang KS, Schelb P, Netzer N, Tavakoli AA, Keymling M, Wehrse E, Hog R, Rotkopf LT, Wennmann M, Glemser PA, Thierjung H, von Knebel Doeberitz N, Kleesiek J, Görtz M, Schütz V, Hielscher T, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein K, Bonekamp D. Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration. Invest Radiol 2022; 57:601-612. [PMID: 35467572 DOI: 10.1097/rli.0000000000000878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI). MATERIALS AND METHODS The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey. Presence of clinically significant PC was determined by the presence of an International Society of Urological Pathology grade 2 or higher PC on combined targeted and extended systematic transperineal MRI/transrectal ultrasound fusion biopsy. Sensitivities and specificities on a patient and prostate sextant basis were compared using the McNemar test and compared with the receiver operating characteristic (ROC) curve of CNN. Survey results were summarized as absolute counts and percentages. RESULTS A total of 201 men were included. The CNN achieved an ROC area under the curve of 0.77 on a patient basis. Using PI-RADS ≥3-emulating probability threshold (c3), CNN had a patient-based sensitivity of 81.8% and specificity of 54.8%, not statistically different from the current clinical routine PI-RADS ≥4 assessment at 90.9% and 54.8%, respectively ( P = 0.30/ P = 1.0). In general, residents achieved similar sensitivity and specificity before and after CNN review. On a prostate sextant basis, clinical assessment possessed the highest ROC area under the curve of 0.82, higher than CNN (AUC = 0.76, P = 0.21) and significantly higher than resident performance before and after CNN review (AUC = 0.76 / 0.76, P ≤ 0.03). The resident survey indicated CNN to be helpful and clinically useful. CONCLUSIONS Pseudoprospective paraclinical integration of fully automated CNN-based detection of suspicious lesions on prostate multiparametric MRI was demonstrated and showed good acceptance among residents, whereas no significant improvement in resident performance was found. General CNN performance was preserved despite an observed shift in CNN calibration, identifying the requirement for continuous quality control and recalibration.
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Affiliation(s)
- Kevin Sun Zhang
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Myriam Keymling
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Eckhard Wehrse
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Robert Hog
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Markus Wennmann
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Heidi Thierjung
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center
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11
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Moroianu ŞL, Bhattacharya I, Seetharaman A, Shao W, Kunder CA, Sharma A, Ghanouni P, Fan RE, Sonn GA, Rusu M. Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning. Cancers (Basel) 2022; 14:2821. [PMID: 35740487 PMCID: PMC9220816 DOI: 10.3390/cancers14122821] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/28/2022] [Accepted: 06/03/2022] [Indexed: 02/04/2023] Open
Abstract
The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of 0.72 at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning.
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Affiliation(s)
| | - Indrani Bhattacharya
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; (I.B.); (W.S.); (A.S.); (P.G.); (G.A.S.)
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Arun Seetharaman
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA;
| | - Wei Shao
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; (I.B.); (W.S.); (A.S.); (P.G.); (G.A.S.)
| | - Christian A. Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Avishkar Sharma
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; (I.B.); (W.S.); (A.S.); (P.G.); (G.A.S.)
| | - Pejman Ghanouni
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; (I.B.); (W.S.); (A.S.); (P.G.); (G.A.S.)
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Richard E. Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Geoffrey A. Sonn
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; (I.B.); (W.S.); (A.S.); (P.G.); (G.A.S.)
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Mirabela Rusu
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA; (I.B.); (W.S.); (A.S.); (P.G.); (G.A.S.)
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12
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Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040799. [PMID: 35453847 PMCID: PMC9027206 DOI: 10.3390/diagnostics12040799] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 02/04/2023] Open
Abstract
Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.
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13
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The Role of Artificial Intelligence in Early Cancer Diagnosis. Cancers (Basel) 2022; 14:cancers14061524. [PMID: 35326674 PMCID: PMC8946688 DOI: 10.3390/cancers14061524] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/01/2023] Open
Abstract
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.
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14
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Bertelli E, Mercatelli L, Marzi C, Pachetti E, Baccini M, Barucci A, Colantonio S, Gherardini L, Lattavo L, Pascali MA, Agostini S, Miele V. Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI. Front Oncol 2022; 11:802964. [PMID: 35096605 PMCID: PMC8792745 DOI: 10.3389/fonc.2021.802964] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/07/2021] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.
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Affiliation(s)
- Elena Bertelli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Laura Mercatelli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Chiara Marzi
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council of Italy (CNR), Sesto Fiorentino, Italy
| | - Eva Pachetti
- "Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, Italy.,Department of Information Engineering (DII), University of Pisa, Pisa, Italy
| | - Michela Baccini
- "Giuseppe Parenti" Department of Statistics, Computer Science, Applications(DiSIA), University of Florence, Florence, Italy.,Florence Center for Data Science, University of Florence, Florence, Italy
| | - Andrea Barucci
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council of Italy (CNR), Sesto Fiorentino, Italy
| | - Sara Colantonio
- "Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, Italy
| | - Luca Gherardini
- "Giuseppe Parenti" Department of Statistics, Computer Science, Applications(DiSIA), University of Florence, Florence, Italy
| | - Lorenzo Lattavo
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Maria Antonietta Pascali
- "Alessandro Faedo" Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, Italy
| | - Simone Agostini
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Florence, Italy
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15
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Duran A, Dussert G, Rouviére O, Jaouen T, Jodoin PM, Lartizien C. ProstAttention-Net: a deep attention model for prostate cancer segmentation by aggressiveness in MRI scans. Med Image Anal 2022; 77:102347. [DOI: 10.1016/j.media.2021.102347] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 12/20/2021] [Accepted: 12/31/2021] [Indexed: 11/27/2022]
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16
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Mourmouris P, Tzelves L, Feretzakis G, Kalles D, Manolitsis I, Berdempes M, Varkarakis I, Skolarikos A. The use and applicability of machine learning algorithms in predicting the surgical outcome for patients with benign prostatic enlargement. Which model to use? Arch Ital Urol Androl 2021; 93:418-424. [PMID: 34933537 DOI: 10.4081/aiua.2021.4.418] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 09/22/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES Artificial intelligence (AI) is increasingly used in medicine, but data on benign prostatic enlargement (BPE) management are lacking. This study aims to test the performance of several machine learning algorithms, in predicting clinical outcomes during BPE surgical management. METHODS Clinical data were extracted from a prospectively collected database for 153 men with BPE, treated with transurethral resection (monopolar or bipolar) or vaporization of the prostate. Due to small sample size, we applied a method for increasing our dataset, Synthetic Minority Oversampling Technique (SMOTE). The new dataset created with SMOTE has been expanded by 453 synthetic instances, in addition to the original 153. The WEKA Data Mining Software was used for constructing predictive models, while several appropriate statistical measures, like Correlation coefficient (R), Mean Absolute Error (MAE), Root Mean-Squared Error (RMSE), were calculated with several supervised regression algorithms - techniques (Linear Regression, Multilayer Perceptron, SMOreg, k-Nearest Neighbors, Bagging, M5Rules, M5P - Pruned Model Tree, and Random forest). RESULTS The baseline characteristics of patients were extracted, with age, prostate volume, method of operation, baseline Qmax and baseline IPSS being used as independent variables. Using the Random Forest algorithm resulted in values of R, MAE, RMSE that indicate the ability of these models to better predict % Qmax increase. The Random Forest model also demonstrated the best results in R, MAE, RMSE for predicting % IPSS reduction. CONCLUSIONS Machine Learning techniques can be used for making predictions regarding clinical outcomes of surgical BPRE management. Wider-scale validation studies are necessary to strengthen our results in choosing the best model.
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Affiliation(s)
- Panagiotis Mourmouris
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanogleio General Hospital, Athens.
| | - Lazaros Tzelves
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanogleio General Hospital, Athens.
| | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, Patras; Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, Marousi.
| | - Dimitris Kalles
- School of Science and Technology, Hellenic Open University, Patras.
| | - Ioannis Manolitsis
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanogleio General Hospital, Athens.
| | - Marinos Berdempes
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanogleio General Hospital, Athens.
| | - Ioannis Varkarakis
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanogleio General Hospital, Athens.
| | - Andreas Skolarikos
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanogleio General Hospital, Athens.
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17
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Hammouda K, Khalifa F, El-Melegy M, Ghazal M, Darwish HE, Abou El-Ghar M, El-Baz A. A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens. SENSORS (BASEL, SWITZERLAND) 2021; 21:6708. [PMID: 34695922 PMCID: PMC8538079 DOI: 10.3390/s21206708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/01/2021] [Accepted: 10/04/2021] [Indexed: 11/16/2022]
Abstract
Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs' edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (CNNS), 150 × 150 (CNNM), and 200 × 200 (CNNL), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The (CNNL) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70-0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems' results, we used the standard ResNet50 and VGG-16 to compare our CNN's patch-wise classification results. As well, we compared the GG's results with that of the previous work.
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Affiliation(s)
- Kamal Hammouda
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (K.H.); (F.K.)
| | - Fahmi Khalifa
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (K.H.); (F.K.)
| | - Moumen El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut 71515, Egypt;
| | - Mohamed Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Hanan E. Darwish
- Mathematics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt;
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Ayman El-Baz
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (K.H.); (F.K.)
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18
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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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John J, Ravikumar A, Abraham B. Prostate cancer prediction from multiple pretrained computer vision model. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00586-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Razek AAKA, El-Diasty T, Elhendy A, Fahmy D, El-Adalany MA. Prostate Imaging Reporting and Data System (PI-RADS): What the radiologists need to know? Clin Imaging 2021; 79:183-200. [PMID: 34098371 DOI: 10.1016/j.clinimag.2021.05.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 01/14/2023]
Abstract
We aim to review the new modifications in MR imaging technique, image interpretation, lexicon, and scoring system of the last version of Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) in a simple and practical way. This last version of PI-RADS v2.1 describes the new technical modifications in the protocol of Multiparametric MRI (MpMRI) including T2, diffusion-weighted imaging (DWI), and dynamic contrast enhancement (DCE) parameters. It includes also; new guidelines in the image interpretation specifications in new locations (lesions located in the central zone and anterior fibromuscular stroma), clarification of T2 scoring of lesions of the transition zone, the distinction between DWI score 2 and 3 lesions in the transition zone and peripheral zone, as well as between positive and negative enhancement in DCE. Biparametric MRI (BpMRI) along with simplified PI-RADS is gaining more acceptances in the assessment of clinically significant prostatic cancer.
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Affiliation(s)
| | - Tarek El-Diasty
- Department of Diagnostic Radiology, Mansoura Urology and Nephrology Center, Mansoura, Egypt
| | - Ahmed Elhendy
- Department of Diagnostic Radiology, Mansoura Urology and Nephrology Center, Mansoura, Egypt
| | - Dalia Fahmy
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, Egypt
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21
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Precise Identification of Prostate Cancer from DWI Using Transfer Learning. SENSORS 2021; 21:s21113664. [PMID: 34070290 PMCID: PMC8197382 DOI: 10.3390/s21113664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/23/2022]
Abstract
Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.
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Brodie A, Dai N, Teoh JYC, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021; 39:379-399. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/20/2020] [Accepted: 03/21/2021] [Indexed: 01/16/2023]
Abstract
There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.
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Affiliation(s)
- Andrew Brodie
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Nick Dai
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Prokar Dasgupta
- Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Nikhil Vasdev
- Hertfordshire and Bedfordshire Urological Cancer Centre, Department of Urology, Lister Hospital, Stevenage, United Kingdom; School of Medicine and Life Sciences, University of Hertfordshire, Hatfield, United Kingdom.
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23
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Cui M, Zhang DY. Artificial intelligence and computational pathology. J Transl Med 2021; 101:412-422. [PMID: 33454724 PMCID: PMC7811340 DOI: 10.1038/s41374-020-00514-0] [Citation(s) in RCA: 151] [Impact Index Per Article: 50.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/08/2020] [Accepted: 11/10/2020] [Indexed: 02/07/2023] Open
Abstract
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.
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Affiliation(s)
- Miao Cui
- St. Luke's Roosevelt Hospital Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10025, USA
| | - David Y Zhang
- Pathology and Laboratory Services, VA Medical Center, New York, NY, 10010, USA.
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Aslim EJ, Law YXT, Fook-Chong SMC, Ho HSS, Yuen JSP, Lau WKO, Lee LS, Cheng CWS, Ngo NT, Law YM, Tay KJ. Defining prostate cancer size and treatment margin for focal therapy: does intralesional heterogeneity impact the performance of multiparametric MRI? BJU Int 2021; 128:178-186. [PMID: 33539650 PMCID: PMC8360156 DOI: 10.1111/bju.15355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To evaluate the impact of intralesional heterogeneity on the performance of multiparametric magnetic resonance imaging (mpMRI) in determining cancer extent and treatment margins for focal therapy (FT) of prostate cancer. PATIENTS AND METHODS We identified men who underwent primary radical prostatectomy for organ- confined prostate cancer over a 3-year period. Cancer foci on whole-mount histology were marked out, coding low-grade (LG; Gleason 3) and high-grade (HG; Gleason 4-5) components separately. Measurements of entire tumours were grouped according to intralesional proportion of HG cancer: 0%, <50% and ≥50%; the readings were corrected for specimen shrinkage and correlated with matching lesions on mpMRI. Separate measurements were also taken of HG cancer components only, and correlated against entire lesions on mpMRI. Size discrepancies were used to derive the optimal tumour size and treatment margins for FT. RESULTS There were 122 MRI-detected cancer lesions in 70 men. The mean linear specimen shrinkage was 8.4%. The overall correlation between histology and MRI dimensions was r = 0.79 (P < 0.001). Size correlation was superior for tumours with high burden (≥50%) compared to low burden (<50%) of HG cancer (r = 0.84 vs r = 0.63; P = 0.007). Size underestimation by mpMRI was more likely for larger tumours (51% for >12 mm vs 26% for ≤12 mm) and those containing HG cancer (44%, vs 20% for LG only). Size discrepancy analysis suggests an optimal tumour size of ≤12 mm and treatment margins of 5-6 mm for FT. For tumours ≤12 mm in diameter, applying 5- and 6-mm treatment margins would achieve 98.6% and 100% complete tumour ablation, respectively. For tumours of all sizes, using the same margins would ablate >95% of the HG cancer components. CONCLUSIONS Multiparametric MRI performance in estimating prostate cancer size, and consequently the treatment margin for FT, is impacted by tumour size and the intralesional heterogeneity of cancer grades.
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Affiliation(s)
| | - Yu Xi Terence Law
- Department of Urology, National University Hospital, Singapore City, Singapore
| | | | - Henry Sun Sien Ho
- Department of Urology, Singapore General Hospital, Singapore City, Singapore
| | - John Shyi Peng Yuen
- Department of Urology, Singapore General Hospital, Singapore City, Singapore
| | - Weber Kam On Lau
- Department of Urology, Singapore General Hospital, Singapore City, Singapore
| | - Lui Shiong Lee
- Department of Urology, Sengkang General Hospital, Singapore City, Singapore
| | | | - Nye Thane Ngo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore City, Singapore
| | - Yan Mee Law
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore City, Singapore
| | - Kae Jack Tay
- Department of Urology, Singapore General Hospital, Singapore City, Singapore
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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26
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Zhang B, Qi S, Pan X, Li C, Yao Y, Qian W, Guan Y. Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma. Front Oncol 2021; 10:598721. [PMID: 33643902 PMCID: PMC7907520 DOI: 10.3389/fonc.2020.598721] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 12/17/2020] [Indexed: 12/12/2022] Open
Abstract
To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.
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Affiliation(s)
- Baihua Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Xiaohuan Pan
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Wei Qian
- Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX, United States
| | - Yubao Guan
- Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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27
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A Fully Automatic Procedure for Brain Tumor Segmentation from Multi-Spectral MRI Records Using Ensemble Learning and Atlas-Based Data Enhancement. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020564] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The accurate and reliable segmentation of gliomas from magnetic resonance image (MRI) data has an important role in diagnosis, intervention planning, and monitoring the tumor’s evolution during and after therapy. Segmentation has serious anatomical obstacles like the great variety of the tumor’s location, size, shape, and appearance and the modified position of normal tissues. Other phenomena like intensity inhomogeneity and the lack of standard intensity scale in MRI data represent further difficulties. This paper proposes a fully automatic brain tumor segmentation procedure that attempts to handle all the above problems. Having its foundations on the MRI data provided by the MICCAI Brain Tumor Segmentation (BraTS) Challenges, the procedure consists of three main phases. The first pre-processing phase prepares the MRI data to be suitable for supervised classification, by attempting to fix missing data, suppressing the intensity inhomogeneity, normalizing the histogram of observed data channels, generating additional morphological, gradient-based, and Gabor-wavelet features, and optionally applying atlas-based data enhancement. The second phase accomplishes the main classification process using ensembles of binary decision trees and provides an initial, intermediary labeling for each pixel of test records. The last phase reevaluates these intermediary labels using a random forest classifier, then deploys a spatial region growing-based structural validation of suspected tumors, thus achieving a high-quality final segmentation result. The accuracy of the procedure is evaluated using the multi-spectral MRI records of the BraTS 2015 and BraTS 2019 training data sets. The procedure achieves high-quality segmentation results, characterized by average Dice similarity scores of up to 86%.
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28
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Shah M, Naik N, Somani BK, Hameed BMZ. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study. Turk J Urol 2020; 46:S27-S39. [PMID: 32479253 PMCID: PMC7731952 DOI: 10.5152/tud.2020.20117] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 04/12/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. MATERIAL AND METHODS Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. RESULTS The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively. CONCLUSIONS In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.
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Affiliation(s)
- Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
| | - Nithesh Naik
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K. Somani
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Urological Surgery, University Hospital Southampton NHS Trust, Southampton, UK
| | - BM Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- KMC Innovation Centre, Manipal Academy of Higher Education, Manipal, Karnataka, India
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Mannaerts CK, Engelbrecht MRW, Postema AW, van Kollenburg RAA, Hoeks CMA, Savci-Heijink CD, Van Sloun RJG, Wildeboer RR, De Reijke TM, Mischi M, Wijkstra H. Detection of clinically significant prostate cancer in biopsy-naïve men: direct comparison of systematic biopsy, multiparametric MRI- and contrast-ultrasound-dispersion imaging-targeted biopsy. BJU Int 2020; 126:481-493. [PMID: 32315112 DOI: 10.1111/bju.15093] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVES To compare and evaluate a multiparametric magnetic resonance imaging (mpMRI)-targeted biopsy (TBx) strategy, contrast-ultrasound-dispersion imaging (CUDI)-TBx strategy and systematic biopsy (SBx) strategy for the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve men. PATIENTS AND METHODS A prospective, single-centre paired diagnostic study included 150 biopsy-naïve men, from November 2015 to November 2018. All men underwent pre-biopsy mpMRI and CUDI followed by a 12-core SBx taken by an operator blinded from the imaging results. Men with suspicious lesions on mpMRI and/or CUDI also underwent MRI-TRUS fusion-TBx and/or cognitive CUDI-TBx after SBx by a second operator. A non-inferiority analysis of the mpMRI- and CUDI-TBx strategies in comparison with SBx for International Society of Urological Pathology Grade Group [GG] ≥2 PCa in any core with a non-inferiority margin of 1 percentage point was performed. Additional analyses for GG ≥2 PCa with cribriform growth pattern and/or intraductal carcinoma (CR/IDC), and GG ≥3 PCa were performed. Differences in detection rates were tested using McNemar's test with adjusted Wald confidence intervals. RESULTS After enrolment of 150 men, an interim analysis was performed. Both the mpMRI- and CUDI-TBx strategies were inferior to SBx for GG ≥2 PCa detection and the study was stopped. SBx found significantly more GG ≥2 PCa: 39% (56/142), as compared with 29% (41/142) and 28% (40/142) for mpMRI-TBx and CUDI-TBx, respectively (P < 0.05). SBx found significantly more GG = 1 PCa: 14% (20/142) compared to 1% (two of 142) and 3% (four of 142) with mpMRI-TBx and CUDI-TBx, respectively (P < 0.05). Detection of GG ≥2 PCa with CR/IDC and GG ≥3 PCa did not differ significantly between the strategies. The mpMRI- and CUDI-TBx strategies were comparable in detection but the mpMRI-TBx strategy had less false-positive findings (18% vs 53%). CONCLUSIONS In our study in biopsy-naïve men, the mpMRI- and CUDI-TBx strategies had comparable PCa detection rates, but the mpMRI-TBX strategy had the least false-positive findings. Both strategies were inferior to SBx for the detection of GG ≥2 PCa, despite reduced detection of insignificant GG = 1 PCa. Both strategies did not significantly differ from SBx for the detection of GG ≥2 PCa with CR/IDC and GG ≥3 PCa.
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Affiliation(s)
- Christophe K Mannaerts
- Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Marc R W Engelbrecht
- Department of Radiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Arnoud W Postema
- Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Rob A A van Kollenburg
- Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Caroline M A Hoeks
- Department of Radiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Cemile Dilara Savci-Heijink
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Ruud J G Van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Rogier R Wildeboer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Theo M De Reijke
- Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Hessel Wijkstra
- Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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30
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Correas JM, Halpern EJ, Barr RG, Ghai S, Walz J, Bodard S, Dariane C, de la Rosette J. Advanced ultrasound in the diagnosis of prostate cancer. World J Urol 2020; 39:661-676. [PMID: 32306060 DOI: 10.1007/s00345-020-03193-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 03/30/2020] [Indexed: 12/17/2022] Open
Abstract
The diagnosis of prostate cancer (PCa) can be challenging due to the limited performance of current diagnostic tests, including PSA, digital rectal examination and transrectal conventional US. Multiparametric MRI has improved PCa diagnosis and is recommended prior to biopsy; however, mp-MRI does miss a substantial number of PCa. Advanced US modalities include transrectal prostate elastography and contrast-enhanced US, as well as improved B-mode, micro-US and micro-Doppler techniques. These techniques can be combined to define a novel US approach, multiparametric US (mp-US). Mp-US improves PCa diagnosis but is not sufficiently accurate to obviate the utility of mp-MRI. Mp-US using advanced techniques and mp-MRI provide complementary information which will become even more important in the era of focal therapy, where precise identification of PCa location is needed.
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Affiliation(s)
- Jean-Michel Correas
- Department of Adult Radiology, Paris University and Necker University Hospital, 149 rue de Sèvres, 75015, Paris Cedex 15, France.
| | - Ethan J Halpern
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Richard G Barr
- Department of Radiology, Northeastern Ohio Medical University, Rootstown, OH, USA
| | - Sangeet Ghai
- Department of Medical Imaging, Princess Margaret Cancer Centre and University of Toronto, Toronto, ON, Canada
| | - Jochen Walz
- Department of Urology, Institut Paoli-Calmettes Cancer Centre, Marseille, France
| | - Sylvain Bodard
- Department of Adult Radiology, Paris University and Necker University Hospital, 149 rue de Sèvres, 75015, Paris Cedex 15, France
| | - Charles Dariane
- Department of Urology, Paris University and European Hospital Georges Pompidou, Paris, France
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