1
|
Kremer LE, Chapman AB, Armato SG. Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype. J Med Imaging (Bellingham) 2023; 10:064503. [PMID: 38156331 PMCID: PMC10752557 DOI: 10.1117/1.jmi.10.6.064503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Purpose Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome. Approach The effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations (z -score, reference-tissue, and original image), gray-level discretization, and upsampling and downsampling pixel schemes. A second dataset for genotype classification from 136 subjects T2W-FS MRI images previously enrolled in the HALT Progression of Polycystic Kidney Disease study was matched for age, gender, and Mayo imaging classification class. Genotype classification was performed using a logistic regression classifier and radiomic features extracted from (1) the noncystic kidney parenchyma and (2) the entire kidney. The area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance across preprocessing methods. Results Radiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively. Conclusions Reproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.
Collapse
Affiliation(s)
- Linnea E. Kremer
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
| | - Arlene B. Chapman
- The University of Chicago, Department of Medicine, Chicago, Illinois, United States
| | - Samuel G. Armato
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
| |
Collapse
|
2
|
Kim H, Kang SW, Kim JH, Nagar H, Sabuncu M, Margolis DJA, Kim CK. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges. Eur J Radiol 2023; 165:110887. [PMID: 37245342 DOI: 10.1016/j.ejrad.2023.110887] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 05/30/2023]
Abstract
Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.
Collapse
Affiliation(s)
- Heejong Kim
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Shin Won Kang
- Research Institute for Future Medicine, Samsung Medical Center, Republic of Korea
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medical College, 525 E 68th St, New York, NY 10021, United States
| | - Mert Sabuncu
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Daniel J A Margolis
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
| |
Collapse
|
3
|
Patsanis A, Sunoqrot MRS, Bathen TF, Elschot M. CROPro: a tool for automated cropping of prostate magnetic resonance images. J Med Imaging (Bellingham) 2023; 10:024004. [PMID: 36895761 PMCID: PMC9990132 DOI: 10.1117/1.jmi.10.2.024004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/09/2023] [Indexed: 03/09/2023] Open
Abstract
Purpose To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images. Approach CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes ( 64 × 64 , 128 × 128 , and 256 × 256 pixels2), pixel spacing ( 0.2 × 0.2 , 0.3 × 0.3 , 0.4 × 0.4 , and 0.5 × 0.5 mm 2 ), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images ( N = 1475 ) from the online available PI-CAI challenge were used to train ( N = 1033 ), validate ( N = 221 ), and test ( N = 221 ) all models. Results Among CNNs, SqueezeNet with stride cropping (image size: 128 × 128 , pixel spacing: 0.2 × 0.2 mm 2 ) achieved the best classification performance ( 0.678 ± 0.006 ). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5 mm 2 ) achieved the best performance ( 0.756 ± 0.009 ). Model performance depended on the cropped area, with optimal size generally larger with center cropping ( ∼ 40 cm 2 ) than random/stride cropping ( ∼ 10 cm 2 ). Conclusion We found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.
Collapse
Affiliation(s)
- Alexandros Patsanis
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
| | - Mohammed R. S. Sunoqrot
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
| | - Tone F. Bathen
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
| | - Mattijs Elschot
- Norwegian University of Science and Technology, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Department of Radiology and Nuclear Medicine, Trondheim, Norway
| |
Collapse
|
4
|
A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer. J Clin Med 2022; 12:jcm12010140. [PMID: 36614941 PMCID: PMC9821561 DOI: 10.3390/jcm12010140] [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: 11/08/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Pooling radiomic features coming from different centers in a statistical framework is challenging due to the variability in scanner models, acquisition protocols, and reconstruction settings. To remove technical variability, commonly called batch effects, different statistical harmonization strategies have been widely used in genomics but less considered in radiomics. The aim of this work was to develop a framework of analysis to facilitate the harmonization of multicenter radiomic features extracted from prostate T2-weighted magnetic resonance imaging (MRI) and to improve the power of radiomics for prostate cancer (PCa) management in order to develop robust non-invasive biomarkers translating into clinical practice. To remove technical variability and correct for batch effects, we investigated four different statistical methods (ComBat, SVA, Arsynseq, and mixed effect). The proposed approaches were evaluated using a dataset of 210 prostate cancer (PCa) patients from two centers. The impacts of the different statistical approaches were evaluated by principal component analysis and classification methods (LogitBoost, random forest, K-nearest neighbors, and decision tree). The ComBat method outperformed all other methods by achieving 70% accuracy and 78% AUC with the random forest method to automatically classify patients affected by PCa. The proposed statistical framework enabled us to define and develop a standardized pipeline of analysis to harmonize multicenter T2W radiomic features, yielding great promise to support PCa clinical practice.
Collapse
|
5
|
Sunoqrot MRS, Saha A, Hosseinzadeh M, Elschot M, Huisman H. Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges. Eur Radiol Exp 2022; 6:35. [PMID: 35909214 PMCID: PMC9339427 DOI: 10.1186/s41747-022-00288-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/09/2022] [Indexed: 11/29/2022] Open
Abstract
Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T (n = 3,018; 89.6%) or 1.5 T (n = 296; 8.8%), 346 cases scanned with endorectal coil (10.3%), 3,023 (89.7%) with phased-array surface coils; 412 collected for anatomical segmentation tasks, 3,096 for PCa detection/classification; for 2,240 cases lesions delineation is available and 56 cases have matching histopathologic images; for 2,620 cases the PSA level is provided; the total size of all open datasets amounts to approximately 253 GB. Of note, quality of annotations provided per dataset highly differ and attention must be paid when using these datasets (e.g., data overlap). Seven grand challenges and commercial applications from eleven vendors are here considered. Few small studies provided prospective validation. More work is needed, in particular validation on large-scale multi-institutional, well-curated public datasets to test general applicability. Moreover, AI needs to be explored for clinical stages other than detection/characterization (e.g., follow-up, prognosis, interventions, and focal treatment).
Collapse
Affiliation(s)
- Mohammed R S Sunoqrot
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway. .,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway.
| | - Anindo Saha
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands
| | - Matin Hosseinzadeh
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Henkjan Huisman
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway.,Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands
| |
Collapse
|
6
|
Pseudo-T2 mapping for normalization of T2-weighted prostate MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:573-585. [PMID: 35150363 PMCID: PMC9363383 DOI: 10.1007/s10334-022-01003-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 12/22/2021] [Accepted: 01/23/2022] [Indexed: 01/04/2023]
Abstract
Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results AutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB). Discussion All AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s. Supplementary Information The online version contains supplementary material available at 10.1007/s10334-022-01003-9.
Collapse
|
7
|
Ayyad SM, Badawy MA, Shehata M, Alksas A, Mahmoud A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. A New Framework for Precise Identification of Prostatic Adenocarcinoma. SENSORS 2022; 22:s22051848. [PMID: 35270995 PMCID: PMC8915102 DOI: 10.3390/s22051848] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 02/01/2023]
Abstract
Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.
Collapse
Affiliation(s)
- Sarah M. Ayyad
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Mohamed A. Badawy
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (M.A.B.); (M.A.E.-G.)
| | - Mohamed Shehata
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Ahmed Alksas
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Ali Mahmoud
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (M.A.B.); (M.A.E.-G.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Moumen El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut 71511, Egypt;
| | - Nahla B. Abdel-Hamid
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Labib M. Labib
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - H. Arafat Ali
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
- Faulty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35516, Egypt
| | - Ayman El-Baz
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
- Correspondence:
| |
Collapse
|
8
|
Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics 2021; 41:1676-1697. [PMID: 34597215 DOI: 10.1148/rg.2021210020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The classic prostate cancer (PCa) diagnostic pathway that is based on prostate-specific antigen (PSA) levels and the findings of digital rectal examination followed by systematic biopsy has shown multiple limitations. The use of multiparametric MRI (mpMRI) is now widely accepted in men with clinical suspicion for PCa. In addition, clinical information, PSA density, risk calculators, and genomic and other "omics" biomarkers are being used to improve risk stratification. On the basis of mpMRI and MRI-targeted biopsies (MRI-TBx), new diagnostic pathways have been established, aiming to improve the limitations of the classic diagnostic approach. However, these pathways still show limitations associated with mpMRI and MRI-TBx. Definitive PCa diagnosis is made on the basis of histopathologic Gleason grading, which has demonstrated an excellent correlation with clinical outcomes. However, Gleason grading is done subjectively by pathologists and involves poor reproducibility, and PCa may have a heterogeneous distribution of histologic patterns. Thus, important discrepancies persist between biopsy tumor grading and final whole-organ pathologic assessment after radical prostatectomy. PCa offers a unique opportunity to establish a real radiologic-pathologic correlation, as whole-mount radical prostatectomy specimens permit a complete spatial relationship with mpMRI. Artificial intelligence is increasingly being applied to radiologic and pathologic images to improve clinical accuracy and efficiency in PCa diagnosis. This review delineates current PCa diagnostic pathways, with a focus on the role of mpMRI, MRI-TBx, and pathologic analysis. An overview of the expected improvements in PCa diagnosis derived from the use of artificial intelligence, integrated radiologic-pathologic systems, and decision support tools for multidisciplinary teams is provided. An invited commentary by Purysko is available online. Online supplemental material is available for this article. ©RSNA, 2021.
Collapse
Affiliation(s)
- Lidia Alcalá Mata
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Juan Antonio Retamero
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Rajan T Gupta
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Roberto García Figueras
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| | - Antonio Luna
- From the Department of Radiology, Clínica Las Nieves, HT Médica, Calle Carmelo Torres Núm 2, 23007 Jaén, Spain (L.A.M., A.L.); Paige.AI, New York, NY (J.A.R.); Department of Radiology, Duke University Medical Center, Durham, NC (R.T.G.); and Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain (R.G.F.)
| |
Collapse
|
9
|
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.
Collapse
|
10
|
Utility of T 2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study. Sci Rep 2021; 11:2085. [PMID: 33483545 PMCID: PMC7822867 DOI: 10.1038/s41598-021-81272-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 01/04/2021] [Indexed: 12/21/2022] Open
Abstract
T2-weighted (T2W) MRI provides high spatial resolution and tissue-specific contrast, but it is predominantly used for qualitative evaluation of prostate anatomy and anomalies. This retrospective multicenter study evaluated the potential of T2W image-derived textural features for quantitative assessment of peripheral zone prostate cancer (PCa) aggressiveness. A standardized preoperative multiparametric MRI was performed on 87 PCa patients across 6 institutions. T2W intensity and apparent diffusion coefficient (ADC) histogram, and T2W textural features were computed from tumor volumes annotated based on whole-mount histology. Spearman correlations were used to evaluate association between textural features and PCa grade groups (i.e. 1–5). Feature utility in differentiating and classifying low-(grade group 1) vs. intermediate/high-(grade group ≥ 2) aggressive cancers was evaluated using Mann–Whitney U-tests, and a support vector machine classifier employing “hold-one-institution-out” cross-validation scheme, respectively. Textural features indicating image homogeneity and disorder/complexity correlated significantly (p < 0.05) with PCa grade groups. In the intermediate/high-aggressive cancers, textural homogeneity and disorder/complexity were significantly lower and higher, respectively, compared to the low-aggressive cancers. The mean classification accuracy across the centers was highest for the combined ADC and T2W intensity-textural features (84%) compared to ADC histogram (75%), T2W histogram (72%), T2W textural (72%) features alone or T2W histogram and texture (77%), T2W and ADC histogram (79%) combined. Texture analysis of T2W images provides quantitative information or features that are associated with peripheral zone PCa aggressiveness and can augment their classification.
Collapse
|
11
|
Sunoqrot MRS, Selnæs KM, Sandsmark E, Nketiah GA, Zavala-Romero O, Stoyanova R, Bathen TF, Elschot M. A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI. Diagnostics (Basel) 2020; 10:E714. [PMID: 32961895 PMCID: PMC7555425 DOI: 10.3390/diagnostics10090714] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/15/2020] [Accepted: 09/16/2020] [Indexed: 12/26/2022] Open
Abstract
Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.
Collapse
Affiliation(s)
- Mohammed R. S. Sunoqrot
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (G.A.N.); (T.F.B.); (M.E.)
| | - Kirsten M. Selnæs
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (G.A.N.); (T.F.B.); (M.E.)
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway;
| | - Elise Sandsmark
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway;
| | - Gabriel A. Nketiah
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (G.A.N.); (T.F.B.); (M.E.)
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway;
| | - Olmo Zavala-Romero
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (O.Z.-R.); (R.S.)
- Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (O.Z.-R.); (R.S.)
| | - Tone F. Bathen
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (G.A.N.); (T.F.B.); (M.E.)
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway;
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (G.A.N.); (T.F.B.); (M.E.)
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway;
| |
Collapse
|