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Maki JH, Patel NU, Ulrich EJ, Dhaouadi J, Jones RW. Part I: prostate cancer detection, artificial intelligence for prostate cancer and how we measure diagnostic performance: a comprehensive review. Curr Probl Diagn Radiol 2024; 53:606-613. [PMID: 38658286 DOI: 10.1067/j.cpradiol.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/14/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
MRI has firmly established itself as a mainstay for the detection, staging and surveillance of prostate cancer. Despite its success, prostate MRI continues to suffer from poor inter-reader variability and a low positive predictive value. The recent emergence of Artificial Intelligence (AI) to potentially improve diagnostic performance shows great potential. Understanding and interpreting the AI landscape as well as ever-increasing research literature, however, is difficult. This is in part due to widely varying study design and reporting techniques. This paper aims to address this need by first outlining the different types of AI used for the detection and diagnosis of prostate cancer, next deciphering how data collection methods, statistical analysis metrics (such as ROC and FROC analysis) and end points/outcomes (lesion detection vs. case diagnosis) affect the performance and limit the ability to compare between studies. Finally, this work explores the need for appropriately enriched investigational datasets and proper ground truth, and provides guidance on how to best conduct AI prostate MRI studies. Published in parallel, a clinical study applying this suggested study design was applied to review and report a multiple-reader multiple-case clinical study of 150 bi-parametric prostate MRI studies across nine readers, measuring physician performance both with and without the use of a recently FDA cleared Artificial Intelligence software.1.
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Affiliation(s)
- Jeffrey H Maki
- University of Colorado Anschutz Medical Center, Department of Radiology, 12401 E 17th Ave (MS L954), Aurora, Colorado, USA.
| | - Nayana U Patel
- University of New Mexico Department of Radiology, Albuquerque, NM, USA
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Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol 2024; 97:763-769. [PMID: 38273675 PMCID: PMC11027289 DOI: 10.1093/bjr/tqae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 09/30/2023] [Accepted: 01/21/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
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Affiliation(s)
- Badera Al Mohammad
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Afnan Aldaradkeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Monther Gharaibeh
- Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, University of Sydney 2006, Sydney, NSW, Australia
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Mehmood M, Abbasi SH, Aurangzeb K, Majeed MF, Anwar MS, Alhussein M. A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI. Front Oncol 2023; 13:1225490. [PMID: 38023149 PMCID: PMC10666634 DOI: 10.3389/fonc.2023.1225490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model's ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.
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Affiliation(s)
- Mubashar Mehmood
- Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan
| | | | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | | | | | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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Li J, Wang K, Li S, Wu P, Wang X, He Y, Tang W. Clinical study of multifactorial diagnosis in prostate biopsy. Prostate 2023; 83:1494-1503. [PMID: 37545333 DOI: 10.1002/pros.24608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/14/2023] [Accepted: 07/24/2023] [Indexed: 08/08/2023]
Abstract
PURPOSE To study the feasibility of using an artificial intelligence (AI) algorithm for the diagnosis of clinically significant prostate cancer (csPCa) on multiparametric MRI (mpMRI) in combination with conventional clinical information. METHODS A retrospective study cohort with 505 patients was collected, with complete information on age (≤60, 60-80, and >80 years), PSA (≤4, 4-10, and >10 ng/dL), and pathology results. The patients with ISUP group >2 were classified as csPCa, and the patients with ISUP = 1 or no evidence of prostate cancer were classified as non-csPCa. The diagnosis of mpMRI was made by experienced radiologists following the prostate imaging reporting and data system (PIRADS ≤ 2, PIRADS = 3, and PIRADS > 3). The mpMRI images were processed by a homemade AI algorithm, and the AI results were obtained as positive or negative for csPCa. Two logistic regression models were fitted, with pathological findings as the dependent variable, that is, a conventional model and an AI model. The conventional model used age, PSA, and PIRADS as the independent variables. The AI model took the AI result and the abovementioned clinical information as the independent variables. The predicted probability of the patients from the conventional model and the AI model were used to test the prediction efficacy of the models. The DeLong test was performed to compare differences in the area under the receiver operating characteristic (ROC) area under the curve (AUC) between the conventional model and the AI model. RESULTS In total, 505 patients were included in the study; 280 were diagnosed with csPCa, and 225 were non-csPCa. The median age was 72.0 (67.0, 76.0) years, with a median PSA value of 13.0 (7.46, 27.5) ng/dL. Statically significant differences were found in age, PSA, PIRADS score and AI results between the csPCa and non-csPCa groups (all p < 0.001). In the multivariable regression models, all the variables were independently associated with csPCa. The conventional model (R2 = 0.361) and the AI model (R2 = 0.474) were compared with analysis of variance (ANOVA) and showed statistically significant differences (χ2 = 63.695, p < 0.001). The AUC of the ROC curve for the conventional model was 0.782 (95% confidence interval [CI]: 0.742-0.823), which was less than the AUC of the AI model with statistical significance (0.849 [95% CI: 0.815-0.883], p < 0.001). CONCLUSION In combination with routine clinical information, such as age, PSA, and PIRADS category, adding information from the AI algorithm based on mpMRI could improve the diagnosis of csPCa.
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Affiliation(s)
- Jialei Li
- Zhejiang Chinese Medical University, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Song Li
- Zhejiang Chinese Medical University, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Yi He
- The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Wenrui Tang
- The Affiliated Hospital of Jiaxing University, Jiaxing, China
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Rafikova G, Gilyazova I, Enikeeva K, Pavlov V, Kzhyshkowska J. Prostate Cancer: Genetics, Epigenetics and the Need for Immunological Biomarkers. Int J Mol Sci 2023; 24:12797. [PMID: 37628978 PMCID: PMC10454494 DOI: 10.3390/ijms241612797] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Epidemiological data highlight prostate cancer as a significant global health issue, with high incidence and substantial impact on patients' quality of life. The prevalence of this disease is associated with various factors, including age, heredity, and race. Recent research in prostate cancer genetics has identified several genetic variants that may be associated with an increased risk of developing the disease. However, despite the significance of these findings, genetic markers for prostate cancer are not currently utilized in clinical practice as reliable indicators of the disease. In addition to genetics, epigenetic alterations also play a crucial role in prostate cancer development. Aberrant DNA methylation, changes in chromatin structure, and microRNA (miRNA) expression are major epigenetic events that influence oncogenesis. Existing markers for prostate cancer, such as prostate-specific antigen (PSA), have limitations in terms of sensitivity and specificity. The cost of testing, follow-up procedures, and treatment for false-positive results and overdiagnosis contributes to the overall healthcare expenditure. Improving the effectiveness of prostate cancer diagnosis and prognosis requires either narrowing the risk group by identifying new genetic factors or enhancing the sensitivity and specificity of existing markers. Immunological biomarkers (both circulating and intra-tumoral), including markers of immune response and immune dysfunction, represent a potentially useful area of research for enhancing the diagnosis and prognosis of prostate cancer. Our review emphasizes the need for developing novel immunological biomarkers to improve the diagnosis, prognosis, and management of prostate cancer. We highlight the most recent achievements in the identification of biomarkers provided by circulating monocytes and tumor-associated macrophages (TAMs). We highlight that monocyte-derived and TAM-derived biomarkers can enable to establish the missing links between genetic predisposition, hormonal metabolism and immune responses in prostate cancer.
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Affiliation(s)
- Guzel Rafikova
- Institute of Urology and Clinical Oncology, Bashkir State Medical University, 450077 Ufa, Russia (K.E.); (V.P.)
| | - Irina Gilyazova
- Institute of Urology and Clinical Oncology, Bashkir State Medical University, 450077 Ufa, Russia (K.E.); (V.P.)
- Institute of Biochemistry and Genetics, Ufa Federal Research Center of the Russian Academy of Sciences, 450054 Ufa, Russia
| | - Kadriia Enikeeva
- Institute of Urology and Clinical Oncology, Bashkir State Medical University, 450077 Ufa, Russia (K.E.); (V.P.)
| | - Valentin Pavlov
- Institute of Urology and Clinical Oncology, Bashkir State Medical University, 450077 Ufa, Russia (K.E.); (V.P.)
| | - Julia Kzhyshkowska
- Laboratory for Translational Cellular and Molecular Biomedicine, Tomsk State University, 634050 Tomsk, Russia
- Genetic Technology Laboratory, Siberian State Medical University, 634050 Tomsk, Russia
- Institute of Transfusion Medicine and Immunology, Mannheim Institute of Innate Immunosciences (MI3), Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
- German Red Cross Blood Service Baden-Württemberg—Hessen, 68167 Mannheim, Germany
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Chaddad A, Tan G, Liang X, Hassan L, Rathore S, Desrosiers C, Katib Y, Niazi T. Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects. Cancers (Basel) 2023; 15:3839. [PMID: 37568655 PMCID: PMC10416937 DOI: 10.3390/cancers15153839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada
| | - Guina Tan
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
| | - Xiaojuan Liang
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
| | - Lama Hassan
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China
| | | | - Christian Desrosiers
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada
| | - Yousef Katib
- Department of Radiology, Taibah University, Al Madinah 42361, Saudi Arabia
| | - Tamim Niazi
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada
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Martinez-Marroquin E, Chau M, Turner M, Haxhimolla H, Paterson C. Use of artificial intelligence in discerning the need for prostate biopsy and readiness for clinical practice: a systematic review protocol. Syst Rev 2023; 12:126. [PMID: 37461083 DOI: 10.1186/s13643-023-02282-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 06/25/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Variability and inaccuracies in the diagnosis of prostate cancer, and the risk of complications from invasive tests, have been extensively reported in the research literature. To address this, the use of artificial intelligence (AI) has been attracting increased interest in recent years to improve the diagnostic accuracy and objectivity. Although AI literature has reported promising results, further research is needed on the identification of evidence gaps that limit the potential adoption in prostate cancer screening practice. METHODS A systematic electronic search strategy will be used to identify peer-reviewed articles published from inception to the date of searches and indexed in CINAHL, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Registries including Cochrane Central Register of Controlled Trials, ClinicalTrials.gov and International Clinical Trials Registry Platform (ICTRP) will be searched for unpublished studies, and experts were invited to provide suitable references. The research and reporting will be based on Cochrane recommendations and PRISMA guidelines, respectively. The screening and quality assessment of the articles will be conducted by two of the authors independently, and conflicts will be resolved by a third author. DISCUSSION This systematic review will summarise the use of AI techniques to predict the need for prostate biopsy based on clinical and demographic indicators, including its diagnostic accuracy and readiness for adoption in clinical practice. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42022336540.
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Affiliation(s)
- Elisa Martinez-Marroquin
- Faculty of Science and Technology, University of Canberra, Canberra, Australian Capital Territory, 2617, Australia.
| | - Minh Chau
- Prehabilitation, Activity, Cancer, Exercise and Survivorship (PACES) Research Group, Faculty of Health, University of Canberra, Canberra, ACT, 2617, Australia
| | - Murray Turner
- Prehabilitation, Activity, Cancer, Exercise and Survivorship (PACES) Research Group, Faculty of Health, University of Canberra, Canberra, ACT, 2617, Australia
| | - Hodo Haxhimolla
- Prehabilitation, Activity, Cancer, Exercise and Survivorship (PACES) Research Group, Faculty of Health, University of Canberra, Canberra, ACT, 2617, Australia
| | - Catherine Paterson
- Prehabilitation, Activity, Cancer, Exercise and Survivorship (PACES) Research Group, Faculty of Health, University of Canberra, Canberra, ACT, 2617, Australia
- Robert Gordon University, Aberdeen, AB10 7AQ, Scotland, UK
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Wang YH, Liang C, Zhu FP, Zhou TR, Li J, Wang ZJ, Liu BJ. Improving the understanding of PI-RADS in practice: characters of PI-RADS 4 and 5 lesions with negative biopsy. Asian J Androl 2023; 25:217-222. [PMID: 36722578 PMCID: PMC10069697 DOI: 10.4103/aja2022112] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
The Prostate Imaging Reporting and Data System (PI-RADS) has good ability to identify the nature of lesions on prostate magnetic resonance imaging (MRI). However, some lesions are still reported as PI-RADS 4 and 5 but are biopsy-proven benign. Herein, we aimed to summarize the reasons for the negative prostate biopsy of patients who were assessed as PI-RADS 4 and 5 by biparameter MRI. We retrospectively sorted out the prostate MRI, treatment, and follow-up results of patients who underwent a biparameter MRI examination of the prostate in The First Affiliated Hospital of Nanjing Medical University (Nanjing, China) from August 2019 to June 2021 with PI-RADS 4 and 5 but a negative biopsy. We focused on reviewing the MRI characteristics. A total of 467 patients underwent transperineal prostate biopsy. Among them, biopsy pathology of 93 cases were negative. After follow-up, 90 patients were ruled out of prostate cancer. Among the 90 cases, 40 were considered to be overestimated PI-RADS after review. A total of 22 cases were transition zone (TZ) lesions with regular appearance and clear boundaries, and 3 cases were symmetrical lesions. Among 15 cases, the TZ nodules penetrated the peripheral zone (PZ) and were mistaken for the origin of PZ. A total of 17 cases of lesions were difficult to distinguish from prostate cancer. Among them, 5 cases were granulomatous inflammation (1 case of prostate tuberculosis). A total of 33 cases were ambiguous lesions, whose performance was between PI-RADS 3 and 4. In summary, the reasons for "false-positive MRI diagnosis" included PI-RADS overestimation, ambiguous images giving higher PI-RADS, diseases that were really difficult to distinguish, and missed lesion in the initial biopsy; and the first two accounted for the most.
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Affiliation(s)
- Yu-Hao Wang
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Chao Liang
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Fei-Peng Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Tian-Ren Zhou
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Jie Li
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zeng-Jun Wang
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Bian-Jiang Liu
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
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9
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Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24:ijms24054615. [PMID: 36902045 PMCID: PMC10003020 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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10
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Singh D, Kumar V, Das CJ, Singh A, Mehndiratta A. Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer. Front Oncol 2022; 12:961985. [PMID: 36505875 PMCID: PMC9730331 DOI: 10.3389/fonc.2022.961985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 10/27/2022] [Indexed: 11/27/2022] Open
Abstract
Background Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods. Methods The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores. Results The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation. Conclusion The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring.
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Affiliation(s)
- Dharmesh Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Virendra Kumar
- Department of Nuclear Magnetic Resonance (NMR), All India Institute of Medical Sciences, New Delhi, India
| | - Chandan J. Das
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India,Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India,Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India,*Correspondence: Amit Mehndiratta,
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11
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Alfano R, Bauman GS, Gomez JA, Gaed M, Moussa M, Chin J, Pautler S, Ward AD. Prostate cancer classification using radiomics and machine learning on mp-MRI validated using co-registered histology. Eur J Radiol 2022; 156:110494. [PMID: 36095953 DOI: 10.1016/j.ejrad.2022.110494] [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: 03/31/2022] [Revised: 07/04/2022] [Accepted: 08/16/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for prostate cancer (PCa) detection but currently has unaddressed limitations. Computer aided diagnosis (CAD) systems have been developed to address these needs, but many approaches used to generate and validate the models have inherent biases. METHOD All clinically significant PCa on histology was mapped to mp-MRI using a previously validated registration algorithm. Shape and size matched non-PCa regions were selected using a proposed sampling algorithm to eliminate biases towards shape and size. Further analysis was performed to assess biases regarding inter-zonal variability. RESULTS A 5-feature Naïve-Bayes classifier produced an area under the receiver operating characteristic curve (AUC) of 0.80 validated using leave-one-patient-out cross-validation. As mean inter-class area mismatch increased, median AUC trended towards positively biasing classifiers to producing higher AUCs. Classifiers were invariant to differences in shape between PCa and non-PCa lesions (AUC: 0.82 vs 0.82). Performance for models trained and tested only in the peripheral zone was found to be lower than in the central gland (AUC: 0.75 vs 0.95). CONCLUSION We developed a radiomics based machine learning system to classify PCa vs non-PCa tissue on mp-MRI validated on accurately co-registered mid-gland histology with a measured target registration error. Potential biases involved in model development were interrogated to provide considerations for future work in this area.
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Affiliation(s)
- Ryan Alfano
- Baines Imaging Research Laboratory, 790 Commissioners Rd E, London, ON N6A 5W9, Canada; Lawson Health Research Institute, 750 Base Line Rd E, London, ON N6C 2R5, Canada; Western University, Department of Medical Biophysics, 1151 Richmond St., London, ON N6A 3K7, Canada.
| | - Glenn S Bauman
- Western University, Department of Medical Biophysics, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada.
| | - Jose A Gomez
- Western University, Department of Pathology and Laboratory Medicine, 1151 Richmond St., London, ON N6A 3K7, Canada.
| | - Mena Gaed
- Western University, Department of Pathology and Laboratory Medicine, 1151 Richmond St., London, ON N6A 3K7, Canada.
| | - Madeleine Moussa
- Western University, Department of Pathology and Laboratory Medicine, 1151 Richmond St., London, ON N6A 3K7, Canada.
| | - Joseph Chin
- Western University, Department of Surgery, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada.
| | - Stephen Pautler
- Western University, Department of Surgery, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada.
| | - Aaron D Ward
- Baines Imaging Research Laboratory, 790 Commissioners Rd E, London, ON N6A 5W9, Canada; Lawson Health Research Institute, 750 Base Line Rd E, London, ON N6C 2R5, Canada; Western University, Department of Medical Biophysics, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada.
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12
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Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers. Radiol Med 2022; 127:1245-1253. [PMID: 36114928 PMCID: PMC9587977 DOI: 10.1007/s11547-022-01555-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022]
Abstract
Objective To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. Materials and methods A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. Results Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). Conclusions AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.
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13
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Sekhoacha M, Riet K, Motloung P, Gumenku L, Adegoke A, Mashele S. Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches. Molecules 2022; 27:molecules27175730. [PMID: 36080493 PMCID: PMC9457814 DOI: 10.3390/molecules27175730] [Citation(s) in RCA: 163] [Impact Index Per Article: 81.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 01/07/2023] Open
Abstract
Simple Summary Prostate cancer affects men of all racial and ethnic groups and leads to higher rates of mortality in those belonging to a lower socioeconomic status due to late detection of the disease. There is growing evidence that suggests the contribution of an individual’s genetic profile to prostate cancer. Currently used prostate cancer treatments have serious adverse effects; therefore, new research is focusing on alternative treatment options such as the use of genetic biomarkers for targeted gene therapy, nanotechnology for controlled targeted treatment, and further exploring medicinal plants for new anticancer agents. In this review, we describe the recent advances in prostate cancer research. Abstract Prostate cancer is one of the malignancies that affects men and significantly contributes to increased mortality rates in men globally. Patients affected with prostate cancer present with either a localized or advanced disease. In this review, we aim to provide a holistic overview of prostate cancer, including the diagnosis of the disease, mutations leading to the onset and progression of the disease, and treatment options. Prostate cancer diagnoses include a digital rectal examination, prostate-specific antigen analysis, and prostate biopsies. Mutations in certain genes are linked to the onset, progression, and metastasis of the cancer. Treatment for localized prostate cancer encompasses active surveillance, ablative radiotherapy, and radical prostatectomy. Men who relapse or present metastatic prostate cancer receive androgen deprivation therapy (ADT), salvage radiotherapy, and chemotherapy. Currently, available treatment options are more effective when used as combination therapy; however, despite available treatment options, prostate cancer remains to be incurable. There has been ongoing research on finding and identifying other treatment approaches such as the use of traditional medicine, the application of nanotechnologies, and gene therapy to combat prostate cancer, drug resistance, as well as to reduce the adverse effects that come with current treatment options. In this article, we summarize the genes involved in prostate cancer, available treatment options, and current research on alternative treatment options.
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Affiliation(s)
- Mamello Sekhoacha
- Department of Pharmacology, University of the Free State, Bloemfontein 9300, South Africa
- Correspondence:
| | - Keamogetswe Riet
- Department of Health Sciences, Central University of Technology, Bloemfontein 9300, South Africa
| | - Paballo Motloung
- Department of Health Sciences, Central University of Technology, Bloemfontein 9300, South Africa
| | - Lemohang Gumenku
- Department of Health Sciences, Central University of Technology, Bloemfontein 9300, South Africa
| | - Ayodeji Adegoke
- Department of Pharmacology, University of the Free State, Bloemfontein 9300, South Africa
- Cancer Research and Molecular Biology Laboratories, Department of Biochemistry, College of Medicine, University of Ibadan, Ibadan 200005, Nigeria
| | - Samson Mashele
- Department of Health Sciences, Central University of Technology, Bloemfontein 9300, South Africa
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14
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Fernandes MC, Yildirim O, Woo S, Vargas HA, Hricak H. The role of MRI in prostate cancer: current and future directions. MAGMA (NEW YORK, N.Y.) 2022; 35:503-521. [PMID: 35294642 PMCID: PMC9378354 DOI: 10.1007/s10334-022-01006-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/16/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.
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Affiliation(s)
- Maria Clara Fernandes
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
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15
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Stamatelatou A, Scheenen TWJ, Heerschap A. Developments in proton MR spectroscopic imaging of prostate cancer. MAGMA (NEW YORK, N.Y.) 2022; 35:645-665. [PMID: 35445307 PMCID: PMC9363347 DOI: 10.1007/s10334-022-01011-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 10/25/2022]
Abstract
In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Tom W J Scheenen
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
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16
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Belue MJ, Turkbey B. Tasks for artificial intelligence in prostate MRI. Eur Radiol Exp 2022; 6:33. [PMID: 35908102 PMCID: PMC9339059 DOI: 10.1186/s41747-022-00287-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022] Open
Abstract
The advent of precision medicine, increasing clinical needs, and imaging availability among many other factors in the prostate cancer diagnostic pathway has engendered the utilization of artificial intelligence (AI). AI carries a vast number of potential applications in every step of the prostate cancer diagnostic pathway from classifying/improving prostate multiparametric magnetic resonance image quality, prostate segmentation, anatomically segmenting cancer suspicious foci, detecting and differentiating clinically insignificant cancers from clinically significant cancers on a voxel-level, and classifying entire lesions into Prostate Imaging Reporting and Data System categories/Gleason scores. Multiple studies in all these areas have shown many promising results approximating accuracies of radiologists. Despite this flourishing research, more prospective multicenter studies are needed to uncover the full impact and utility of AI on improving radiologist performance and clinical management of prostate cancer. In this narrative review, we aim to introduce emerging medical imaging AI paper quality metrics such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Field-Weighted Citation Impact (FWCI), dive into some of the top AI models for segmentation, detection, and classification.
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Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health Bethesda, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health Bethesda, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA.
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17
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Turkbey B, Haider MA. Deep learning-based artificial intelligence applications in prostate MRI: brief summary. Br J Radiol 2022; 95:20210563. [PMID: 34860562 PMCID: PMC8978238 DOI: 10.1259/bjr.20210563] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.
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Affiliation(s)
- Baris Turkbey
- Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
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18
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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19
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Giganti F, Lindner S, Piper JW, Kasivisvanathan V, Emberton M, Moore CM, Allen C. Multiparametric prostate MRI quality assessment using a semi-automated PI-QUAL software program. Eur Radiol Exp 2021; 5:48. [PMID: 34738219 PMCID: PMC8568748 DOI: 10.1186/s41747-021-00245-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/08/2021] [Indexed: 11/10/2022] Open
Abstract
The technical requirements for the acquisition of multiparametric magnetic resonance imaging (mpMRI) of the prostate have been clearly outlined in the Prostate Imaging Reporting and Data System (PI-RADS) guidelines, but there is still huge variability in image quality among centres across the world. It has been difficult to quantify what constitutes a good-quality image, and a first attempt to address this matter has been the publication of the Prostate Imaging Quality (PI-QUAL) score and its dedicated scoring sheet. This score includes the assessment of technical parameters that can be obtained from the DICOM files along with a visual evaluation of certain features on prostate MRI (e.g., anatomical structures). We retrospectively analysed the image quality of 10 scans from different vendors and magnets using a semiautomated dedicated PI-QUAL software program and compared the time needed for assessing image quality using two methods (semiautomated assessment versus manual filling of the scoring sheet). This semiautomated software is able to assess the technical parameters automatically, but the visual assessment is still performed by the radiologist. There was a significant reduction in the reporting time of prostate mpMRI quality according to PI-QUAL using the dedicated software program compared to manual filling (5'54″ versus 7'59″; p = 0.005). A semiautomated PI-QUAL software program allows the radiologist to assess the technical details related to the image quality of prostate mpMRI in a quick and reliable manner, allowing clinicians to have more confidence that the quality of mpMRI of the prostate is sufficient to determine patient care.
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Affiliation(s)
- Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK.
- Division of Surgery & Interventional Science, University College London, 3rd Floor, Charles Bell House, 43-45 Foley St., W1W 7TS, London, UK.
| | | | | | - Veeru Kasivisvanathan
- Division of Surgery & Interventional Science, University College London, 3rd Floor, Charles Bell House, 43-45 Foley St., W1W 7TS, London, UK
- Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Mark Emberton
- Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Caroline M Moore
- Division of Surgery & Interventional Science, University College London, 3rd Floor, Charles Bell House, 43-45 Foley St., W1W 7TS, London, UK
- Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Clare Allen
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
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20
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Bardis M, Houshyar R, Chantaduly C, Tran-Harding K, Ushinsky A, Chahine C, Rupasinghe M, Chow D, Chang P. Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning. Radiol Imaging Cancer 2021; 3:e200024. [PMID: 33929265 DOI: 10.1148/rycan.2021200024] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Purpose To develop a deep learning model to delineate the transition zone (TZ) and peripheral zone (PZ) of the prostate on MR images. Materials and Methods This retrospective study was composed of patients who underwent a multiparametric prostate MRI and an MRI/transrectal US fusion biopsy between January 2013 and May 2016. A board-certified abdominal radiologist manually segmented the prostate, TZ, and PZ on the entire data set. Included accessions were split into 60% training, 20% validation, and 20% test data sets for model development. Three convolutional neural networks with a U-Net architecture were trained for automatic recognition of the prostate organ, TZ, and PZ. Model performance for segmentation was assessed using Dice scores and Pearson correlation coefficients. Results A total of 242 patients were included (242 MR images; 6292 total images). Models for prostate organ segmentation, TZ segmentation, and PZ segmentation were trained and validated. Using the test data set, for prostate organ segmentation, the mean Dice score was 0.940 (interquartile range, 0.930-0.961), and the Pearson correlation coefficient for volume was 0.981 (95% CI: 0.966, 0.989). For TZ segmentation, the mean Dice score was 0.910 (interquartile range, 0.894-0.938), and the Pearson correlation coefficient for volume was 0.992 (95% CI: 0.985, 0.995). For PZ segmentation, the mean Dice score was 0.774 (interquartile range, 0.727-0.832), and the Pearson correlation coefficient for volume was 0.927 (95% CI: 0.870, 0.957). Conclusion Deep learning with an architecture composed of three U-Nets can accurately segment the prostate, TZ, and PZ. Keywords: MRI, Genital/Reproductive, Prostate, Neural Networks Supplemental material is available for this article. © RSNA, 2021.
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Affiliation(s)
- Michelle Bardis
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Roozbeh Houshyar
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Chanon Chantaduly
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Karen Tran-Harding
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Alexander Ushinsky
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Chantal Chahine
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Mark Rupasinghe
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Daniel Chow
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
| | - Peter Chang
- From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.)
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21
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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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Bui TL, Glavis-Bloom J, Chahine C, Mehta R, Wolfe T, Bhatter P, Rupasinghe M, Carbone J, Haider MA, Giganti F, Giona S, Oto A, Lee G, Houshyar R. Prostate minimally invasive procedures: complications and normal vs. abnormal findings on multiparametric magnetic resonance imaging (mpMRI). Abdom Radiol (NY) 2021; 46:4388-4400. [PMID: 33977352 PMCID: PMC8346422 DOI: 10.1007/s00261-021-03097-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/19/2021] [Indexed: 11/25/2022]
Abstract
Minimally invasive alternatives to traditional prostate surgery are increasingly utilized to treat benign prostatic hyperplasia and localized prostate cancer in select patients. Advantages of these treatments over prostatectomy include lower risk of complication, shorter length of hospital stay, and a more favorable safety profile. Multiparametric magnetic resonance imaging (mpMRI) has become a widely accepted imaging modality for evaluation of the prostate gland and provides both anatomical and functional information. As prostate mpMRI and minimally invasive prostate procedure volumes increase, it is important for radiologists to be familiar with normal post-procedure imaging findings and potential complications. This paper reviews the indications, procedural concepts, common post-procedure imaging findings, and potential complications of prostatic artery embolization, prostatic urethral lift, irreversible electroporation, photodynamic therapy, high-intensity focused ultrasound, focal cryotherapy, and focal laser ablation.
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Affiliation(s)
- Thanh-Lan Bui
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Justin Glavis-Bloom
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Chantal Chahine
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Raj Mehta
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Taylor Wolfe
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Param Bhatter
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Mark Rupasinghe
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Joseph Carbone
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
| | - Masoom A. Haider
- Joint Department of Medical Imaging, Sinai Health System, University Health Network, University of Toronto, Toronto, ON Canada
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
- Division of Surgery & Interventional Science, University College London, London, UK
| | - Simone Giona
- Department of Urology, Frimley Park Hospital, Frimley, Camberley UK
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL USA
| | - Grace Lee
- Department of Radiology, University of Chicago, Chicago, IL USA
| | - Roozbeh Houshyar
- Department of Radiological Sciences, University of California, Irvine, Orange, CA 92868-3201 USA
- Department of Radiological Sciences, University of California, Irvine, Bldg. 1 Route 140, Orange, CA 92868-3201 USA
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23
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Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data. Cancers (Basel) 2021; 13:cancers13163944. [PMID: 34439099 PMCID: PMC8391234 DOI: 10.3390/cancers13163944] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/02/2021] [Accepted: 08/02/2021] [Indexed: 11/18/2022] Open
Abstract
Simple Summary Prostate Cancer is one of the main threats to men’s health. Its accurate diagnosis is crucial to properly treat patients depending on the cancer’s level of aggressiveness. Tumor risk-stratification is still a challenging task due to the difficulties met during the reading of multi-parametric Magnetic Resonance Images. Artificial Intelligence models may help radiologists in staging the aggressiveness of the equivocal lesions, reducing inter-observer variability and evaluation time. However, these algorithms need many high-quality images to work efficiently, bringing up overfitting and lack of standardization and reproducibility as emerging issues to be addressed. This study attempts to illustrate the state of the art of current research of Artificial Intelligence methods to stratify prostate cancer for its clinical significance suggesting how widespread use of public databases could be a possible solution to these issues. Abstract Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.
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Abstract
PURPOSE OF REVIEW Artificial intelligence has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular data, clinical variables, etc.) and used the statistical and machine-learning methods to measure the model performance. In this review, we summarized and discussed the most recent radiomic pipeline used for clinical analysis. RECENT FINDINGS Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs. Most artificial intelligence tools are based on imaging features, known as radiomic analysis that can be refined into predictive models in noninvasively acquired imaging data. This review explores the progress of artificial intelligence-based radiomic tools for clinical applications with a brief description of necessary technical steps. Explaining new radiomic approaches based on deep-learning techniques will explain how the new radiomic models (deep radiomic analysis) can benefit from deep convolutional neural networks and be applied on limited data sets. SUMMARY To consider the radiomic algorithms, further investigations are recommended to involve deep learning in radiomic models with additional validation steps on various cancer types.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
| | - Yousef Katib
- Department of Radiology, Taibah University, Al-Madinah, Saudi Arabia
| | - Lama Hassan
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
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25
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Abstract
BACKGROUND Artificial intelligence (AI) has the potential to fundamentally change medicine within the coming decades. Radiological imaging is one of the primary fields of its clinical application. OBJECTIVES In this article, we summarize previous AI developments with a focus on oncological radiology. Based on selected examples, we derive scenarios for developments in the next 10 years. MATERIALS AND METHODS This work is based on a review of various literature and product databases, publications by regulatory authorities, reports, and press releases. CONCLUSIONS The clinical use of AI applications is still in an early stage of development. The large number of research publications shows the potential of the field. Several certified products have already become available to users. However, for a widespread adoption of AI applications in clinical routine, several fundamental prerequisites are still awaited. These include the generation of evidence justifying the use of algorithms through representative clinical studies, adjustments to the framework for approval processes and dedicated education and teaching resources for its users. It is expected that use of AI methods will increase, thus, creating new opportunities for improved diagnostics, therapy, and more efficient workflows.
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Affiliation(s)
- Andreas M Bucher
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Frankfurt am Main, Theodor-Stern Kai 7, 60590, Frankfurt am Main, Deutschland.
| | - Jens Kleesiek
- Translationale bildgestützte Onkologie, Institut für KI in der Medizin (IKIM), Universitätsmedizin Essen, Essen, Deutschland
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26
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Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review. Cancers (Basel) 2021; 13:cancers13030552. [PMID: 33535569 PMCID: PMC7867056 DOI: 10.3390/cancers13030552] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/18/2021] [Accepted: 01/27/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted. Abstract The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.
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27
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Manini C, López JI. Insights into Urological Cancer. Cancers (Basel) 2021; 13:E204. [PMID: 33429960 PMCID: PMC7827315 DOI: 10.3390/cancers13020204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 12/22/2022] Open
Abstract
The year the Covid-19 pandemic appeared has been quite prolific in urological cancer research, and the collection of articles, perspectives, and reviews on renal, prostate, and urinary tract tumors merged in this Urological Cancer 2020 issue is just a representative sample of this assertion [...].
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Affiliation(s)
- Claudia Manini
- Department of Pathology, San Giovanni Bosco Hospital, 10154 Turin, Italy
| | - José I. López
- Department of Pathology, Cruces University Hospital, Biocruces-Bizkaia Health Research Institute, 48903 Barakaldo, Spain
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28
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Abstract
In the past 10 years, the methods of artificial intelligence (AI) have experienced breakthroughs that have opened up a multitude of new fields of application for information technology. AI is particularly strong in those areas where patterns have to be recognized and conclusions and forecasts based on large, multiparametric data sets have to be drawn. Computers are superior to us in terms of precision and speed in these problems. These advances in information technology reach us at a time when innovations in diagnostics and sensor technology enable more precise patient stratification and confront medical personnel with an increasing quantity and quality of patient data. Urology is symbolic of this new complexity of medicine, in which multi-layered diagnostic cascades require a high degree of interdisciplinarity and, especially in uro-oncology, therapeutic strategies are becoming more differentiated and require the interpretation of multiple clinical and diagnostic data. Here, methods of Artificial Intelligence will in future support medical personnel in diagnostics and therapy decisions and thus come closer to the goal of precision medicine. A prerequisite for the success of AI-based support tools will be the transparent development and validation of the software, as well as the population-based visualization of decision parameters.
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