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Chen P, Turco S, Wang Y, Jager A, Daures G, Wijkstra H, Zwart W, Huang P, Mischi M. Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome? ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1194-1202. [PMID: 38734528 DOI: 10.1016/j.ultrasmedbio.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/16/2024] [Accepted: 04/14/2024] [Indexed: 05/13/2024]
Abstract
OBJECTIVES To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. METHODS After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). RESULTS Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier. CONCLUSIONS Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.
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Affiliation(s)
- Peiran Chen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
| | - Simona Turco
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Yao Wang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Auke Jager
- Department of Urology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gautier Daures
- Angiogenesis Analytics, JADS Venture Campus, Netherlands
| | - Hessel Wijkstra
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Urology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Wim Zwart
- Angiogenesis Analytics, JADS Venture Campus, Netherlands
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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Mendes B, Domingues I, Santos J. Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions. J Clin Med 2024; 13:3907. [PMID: 38999473 PMCID: PMC11242211 DOI: 10.3390/jcm13133907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.
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Affiliation(s)
- Bruno Mendes
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Faculty of Engineering of the University of Porto (FEUP), R. Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Inês Domingues
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
| | - João Santos
- Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), Medical Physics, Radiobiology and Radiological Protection Group, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- School of Medicine and Biomedical Sciences (ICBAS), R. Jorge de Viterbo Ferreira 228, 4050-313 Porto, Portugal
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Yu L, Che M, Wu X, Luo H. Research on ultrasound-based radiomics: a bibliometric analysis. Quant Imaging Med Surg 2024; 14:4520-4539. [PMID: 39022291 PMCID: PMC11250334 DOI: 10.21037/qims-23-1867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 05/16/2024] [Indexed: 07/20/2024]
Abstract
Background A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization. Methods A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms. Results Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer. Conclusions Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognostic evaluation of diseases. Additionally, the coupling of artificial intelligence techniques with ultrasound imaging has yielded a number of promising tools that facilitate clinical decision-making and enable the practice of precision medicine. Finally, the development of ultrasound-based radiomics requires multidisciplinary cooperation and joint efforts from the field biomedicine, information technology, statistics, and clinical medicine.
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Affiliation(s)
- Lu Yu
- Department of Ultrasound, The Second Affiliated Hospital of Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Mengting Che
- Department of Tumor Radiotherapy and Chemotherapy, The Second Affiliated Hospital of Sichuan University, Chengdu, China
| | - Xu Wu
- Department of Ultrasound, The Second Affiliated Hospital of Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Hong Luo
- Department of Ultrasound, The Second Affiliated Hospital of Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
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Jager A, Oddens JR, Postema AW, Miclea RL, Schoots IG, Nooijen PGTA, van der Linden H, Barentsz JO, Heijmink SWTPJ, Wijkstra H, Mischi M, Turco S. Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis? Cancers (Basel) 2024; 16:2431. [PMID: 39001493 PMCID: PMC11240399 DOI: 10.3390/cancers16132431] [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: 05/23/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.
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Affiliation(s)
- Auke Jager
- Department of Urology, Amsterdam UMC, University of Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jorg R Oddens
- Department of Urology, Amsterdam UMC, University of Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Arnoud W Postema
- Leiden University Medical Center, Department of Urology, 2333 ZA Leiden, The Netherlands
| | - Razvan L Miclea
- Department of Radiology and Nuclear Imaging, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Ivo G Schoots
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
- Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Peet G T A Nooijen
- Department of Pathology, Jeroen Bosch Hospital, 5223 GZ 's-Hertogenbosch, The Netherlands
| | - Hans van der Linden
- Department of Pathology, Jeroen Bosch Hospital, 5223 GZ 's-Hertogenbosch, The Netherlands
| | - Jelle O Barentsz
- Department of Radiology, Radboud University Nijmegen Medical Center, 6525 GA Nijmegenfi, The Netherlands
| | - Stijn W T P J Heijmink
- Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Simona Turco
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
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Zhu Y, Meng Z, Wu H, Fan X, Lv W, Tian J, Wang K, Nie F. Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:305-315. [PMID: 38052240 DOI: 10.1055/a-2161-9369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
PURPOSE To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA). MATERIALS AND METHODS This study analyzed 280 biopsy-confirmed metastatic CLAs from 280 cancer patients, including 54 from head and neck squamous cell carcinoma (HNSCC), 58 from thyroid cancer (TC), 92 from lung cancer (LC), and 76 from gastrointestinal cancer (GIC). Before biopsy, patients underwent conventional ultrasound (CUS), ultrasound elastography (UE), and contrast-enhanced ultrasound (CEUS). Based on CUS, DLR models using CUS, CUS+UE, CUS+CEUS, and CUS+UE+CEUS data were developed and compared. The best model was integrated with key clinical indicators selected by univariate analysis to achieve the best classification performance. RESULTS All DLR models achieved similar performance with respect to classifying four primary tumor sites of metastatic CLA (AUC:0.708~0.755). After integrating key clinical indicators (age, sex, and neck level), the US+UE+CEUS+clinical model yielded the best performance with an overall AUC of 0.822 in the validation cohort, but there was no significance compared with the basal CUS+clinical model (P>0.05), both of which identified metastasis from HNSCC, TC, LC, and GIC with 0.869 and 0.911, 0.838 and 0.916, 0.750 and 0.610, and 0.829 and 0.769, respectively. CONCLUSION The ultrasound-based DLR model can be used to classify the primary cancer sites of metastatic CLA, and the CUS combined with clinical indicators is adequate to provide a high discriminatory performance. The addition of the combination of UE and CEUS data is expected to further improve performance.
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Affiliation(s)
- Yangyang Zhu
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of the Chinese Academy of Sciences School, Beijing, China
| | - Hao Wu
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Xiao Fan
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Wenhao Lv
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of the Chinese Academy of Sciences School, Beijing, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of the Chinese Academy of Sciences School, Beijing, China
| | - Fang Nie
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China
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Liu Y, Lu D, Xu G, Wang S, Zhou B, Zhang Y, Ye B, Xiang L, Zhang Y, Xu H. Diagnostic accuracy of qualitative and quantitative magnetic resonance imaging-guided contrast-enhanced ultrasound (MRI-guided CEUS) for the detection of prostate cancer: a prospective and multicenter study. LA RADIOLOGIA MEDICA 2024; 129:585-597. [PMID: 38512615 DOI: 10.1007/s11547-024-01758-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 01/03/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE To evaluate the diagnostic value of MRI-guided contrast-enhanced ultrasound (CEUS) for prostate cancer (PCa) diagnosis, and characteristics of PCa in qualitative and quantitative CEUS. MATERIAL AND METHODS This prospective and multicenter study included 250 patients (133 in the training cohort, 57 in the validation cohort and 60 in the test cohort) who underwent MRI, MRI-guided CEUS and prostate biopsy between March 2021 and February 2023. MRI interpretation, qualitative and quantitative CEUS analysis were conducted. Multitree extreme gradient boosting (XGBoost) machine learning-based models were applied to select the eight most important quantitative parameters. Univariate and multivariate logistic regression models were constructed to select independent predictors of PCa. Diagnostic value was determined for MRI, qualitative and quantitative CEUS using the area under receiver operating characteristic curve (AUC). RESULTS The performance of quantitative CEUS was superior to that of the qualitative CEUS and MRI in predicting PCa. The AUC was 0.779 (95%CI 0.70-0.849), 0.756 (95%CI 0.638-0.874) and 0.759 (95%CI 0.638-0.879) of qualitative CEUS, and 0.885 (95%CI 0.831-0.940), 0.802 (95%CI 0.684-0.919) and 0.824 (95%CI 0.713-0.936) of quantitative CEUS in training, validation and test cohort, respectively. Compared with quantitative CEUS, MRI achieved less well performance for AUC 0.811 (95%CI 0.741-0.882, p = 0.099), 0.748 (95%CI 0.628-0.868, p = 0.539) and 0.737 (95%CI 0.602-0.873, p = 0.029), respectively. Moreover, the highest specificity of 80.6% was obtained by quantitative CEUS. CONCLUSION We developed a reliable method of MRI-guided CEUS that demonstrated enhanced performance compared to MRI. The qualitative and quantitative CEUS characteristics will contribute to improved diagnosis of PCa.
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Affiliation(s)
- Yunyun Liu
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China
| | - Dianyuan Lu
- Department of Ultrasound, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences, Shanghai, China
| | - Guang Xu
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China
| | - Shuai Wang
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China
| | - Bangguo Zhou
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China
| | - Ying Zhang
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China
| | - Beibei Ye
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China
| | - Lihua Xiang
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China.
| | - Yifeng Zhang
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.
- Clinical Research Center for Interventional Medicine, School of Medicine, Ultrasound Research and Education Institute, Tongji University, Shanghai, 200072, China.
| | - Huixiong Xu
- Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
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Mukherjee S, Papadopoulos D, Chari N, Ellis D, Charitopoulos K, Charitopoulos I, Bishara S. High-grade prostate cancer demonstrates preferential growth in the cranio-caudal axis and provides discrimination of disease grade in an MRI parametric model. Br J Radiol 2024; 97:574-582. [PMID: 38276882 PMCID: PMC11027337 DOI: 10.1093/bjr/tqad066] [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: 07/12/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES To determine if multiparametric MRI prostate cancer (PC) lesion dimensions in different axes could distinguish between PC, grade group (GG) >2, and GG >3 on targeted transperineal biopsy and create and validate a predictive model on a separate cohort. METHODS The maximum transverse, anterio-posterior, and cranio-caudal lesion dimensions were assessed against the presence of any cancer, GG >2, and GG >3 on biopsy by binary logistic regression. The optimum multivariate models were evaluated on a separate cohort. RESULTS One hundred and ninety-three lesions from 148 patients were evaluated. Increased lesion volume, Prostate Specific Antigen (PSA), Prostate Imaging Reporting and Data System score, and decreased Apparent Diffusion Coefficient (ADC) were associated with increased GG (P < .001). The ratio of cranio-caudal to anterior-posterior lesion dimension increased from 1.20 (95% CI, 1.14-1.25) for GG ≤ 3 to 1.43 (95% CI, 1.28-1.57) for GG > 3 (P = .0022). The cranio-caudal dimension of the lesion was the strongest predictor of GG >3 (P = .000, area under the receiver operator characteristic curve [AUC] = 0.81). The best multivariate models had an AUC of 0.84 for cancer, 0.88 for GG > 2, and 0.89 for GG > 3. These models were evaluated on a separate cohort of 40 patients with 61 lesions. They demonstrated an AUC, sensitivity, and specificity of 0.82, 82.3%, and 55.5%, respectively, for the detection of cancer. For GG > 2, the models achieved an AUC of 0.84, sensitivity of 91.7%, and specificity of 69.4%. Additionally, for GG > 3, the models showed an AUC of 0.92, sensitivity of 88.9%, and specificity of 98.1%. CONCLUSIONS Cranio-caudal lesion dimension when used in conjunction with other parameters can create a model superior to the Prostate Imaging Reporting and Data Systems score in predicting cancer. ADVANCES IN KNOWLEDGE Higher-grade PC has a propensity to grow in the cranio-caudal direction, and this could be factored into MRI-based predictive models of prostate biopsy grade.
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Affiliation(s)
- Subhabrata Mukherjee
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Dimitrios Papadopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Natasha Chari
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - David Ellis
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Konstantinos Charitopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Ivo Charitopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Samuel Bishara
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
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Sun Y, Fang J, Shi Y, Li H, Wang J, Xu J, Zhang B, Liang L. Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection. Abdom Radiol (NY) 2024; 49:141-150. [PMID: 37796326 PMCID: PMC10789837 DOI: 10.1007/s00261-023-04050-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 04/30/2023] [Accepted: 05/02/2023] [Indexed: 10/06/2023]
Abstract
PURPOSE To construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ). METHODS A prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model. RESULTS A total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy. CONCLUSION The prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.
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Affiliation(s)
- Ya Sun
- Department of Ultrasound, Aerospace Center Hospital, 15 Yuquan Road, Haidian District, Beijing, China
| | - Jingyang Fang
- Department of Ultrasound, Aerospace Center Hospital, 15 Yuquan Road, Haidian District, Beijing, China
| | - Yanping Shi
- Department of Ultrasound, Aerospace Center Hospital, 15 Yuquan Road, Haidian District, Beijing, China
| | - Huarong Li
- Department of Ultrasound, Aerospace Center Hospital, 15 Yuquan Road, Haidian District, Beijing, China
| | - Jiajun Wang
- Department of Ultrasound, Aerospace Center Hospital, 15 Yuquan Road, Haidian District, Beijing, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, China
| | - Bao Zhang
- Department of Urology, Aerospace Center Hospital, 15 Yuquan Road, Haidian District, Beijing, China.
| | - Lei Liang
- Department of Ultrasound, Aerospace Center Hospital, 15 Yuquan Road, Haidian District, Beijing, China.
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9
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Huang TL, Lu NH, Huang YH, Twan WH, Yeh LR, Liu KY, Chen TB. Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images. Sci Rep 2023; 13:21849. [PMID: 38071254 PMCID: PMC10710441 DOI: 10.1038/s41598-023-49159-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.
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Affiliation(s)
- Te-Li Huang
- Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung, 81362, Taiwan
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan.
- Department of Pharmacy, Tajen University, No.20, Weixin Rd., Yanpu Township, Pingtung, 90741, Taiwan.
- Department of Radiology, E-DA Hospital, I-Shou University, No.1, Yida Rd., Jiao-Su Village, Yan-Chao District, Kaohsiung, 82445, Taiwan.
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan
| | - Wen-Hung Twan
- Department of Life Sciences, National Taitung University, No.369, Sec. 2, University Rd., Taitung, 95092, Taiwan
| | - Li-Ren Yeh
- Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, No.1, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Hospital, I-Shou University, No.1, Yida Rd., Jiao-Su Village, Yan-Chao District, Kaohsiung, 82445, Taiwan
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan.
- Institute of Statistics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu, 30010, Taiwan.
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10
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Xiang L, Ma S, Xu Y, Jiang L, Guo H, Liu H, Liu Y. Patient-related characteristics predict prostate cancers in men with PI-RADS 4-5 to further optimize the diagnostic performance of MRI. Abdom Radiol (NY) 2023; 48:3766-3773. [PMID: 37776336 DOI: 10.1007/s00261-023-04011-y] [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: 05/17/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 10/02/2023]
Abstract
PURPOSE To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI. MATERIALS AND METHODS The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated. RESULTS A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%. CONCLUSION The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.
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Affiliation(s)
- Lihua Xiang
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji university, Shanghai, 200072, China
| | - Suping Ma
- Department of Medical Ultrasound, Bengbu First People's Hospital, Bengbu, 233000, Anhui, China
| | - Yongqiang Xu
- Department of Medical Ultrasound, Bengbu First People's Hospital, Bengbu, 233000, Anhui, China
| | - Lei Jiang
- Department of Urinary Surgery, Bengbu First People's Hospital, Bengbu, 233000, Anhui, China
| | - Hao Guo
- Department of Urinary Surgery, Bengbu First People's Hospital, Bengbu, 233000, Anhui, China
| | - Hongyan Liu
- Department of Medical Ultrasound, Bengbu First People's Hospital, Bengbu, 233000, Anhui, China
| | - Yunyun Liu
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji university, Shanghai, 200072, China.
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11
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Tang Y, Li X, Jiang Q, Zhai L. Diagnostic accuracy of multiparametric ultrasound in the diagnosis of prostate cancer: systematic review and meta-analysis. Insights Imaging 2023; 14:203. [PMID: 38001351 PMCID: PMC10673798 DOI: 10.1186/s13244-023-01543-1] [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: 08/04/2023] [Accepted: 10/15/2023] [Indexed: 11/26/2023] Open
Abstract
OBJECTIVES Ultrasound (US) technology has recently made advances that have led to the development of modalities including elastography and contrast-enhanced ultrasound. The use of different US modalities in combination may increase the accuracy of PCa diagnosis. This study aims to assess the diagnostic accuracy of multiparametric ultrasound (mpUS) in the PCa diagnosis. METHODS Through September 2023, we searched through Cochrane CENTRAL, PubMed, Embase, Scopus, Web of Science, ClinicalTrial.gov, and Google Scholar for relevant studies. We used standard methods recommended for meta-analyses of diagnostic evaluation. We plot the SROC curve, which stands for summary receiver operating characteristic. To determine how confounding factors affected the results, meta-regression analysis was used. RESULTS Finally, 1004 patients from 8 studies that were included in this research were examined. The diagnostic odds ratio for PCa was 20 (95% confidence interval (CI), 8-49) and the pooled estimates of mpUS for diagnosis were as follows: sensitivity, 0.88 (95% CI, 0.81-0.93); specificity, 0.72 (95% CI, 0.59-0.83); positive predictive value, 0.75 (95% CI, 0.63-0.87); and negative predictive value, 0.82 (95% CI, 0.71-0.93). The area under the SROC curve was 0.89 (95% CI, 0.86-0.92). There was a significant heterogeneity among the studies (p < 0.01). According to meta-regression, both the sensitivity and specificity of mpUS in the diagnosis of clinically significant PCa (csPCa) were inferior to any PCa. CONCLUSION The diagnostic accuracy of mpUS in the diagnosis of PCa is moderate, but the accuracy in the diagnosis of csPCa is significantly lower than any PCa. More relevant research is needed in the future. CRITICAL RELEVANCE STATEMENT This study provides urologists and sonographers with useful data by summarizing the accuracy of multiparametric ultrasound in the detection of prostate cancer. KEY POINTS • Recent studies focused on the role of multiparametric ultrasound in the diagnosis of prostate cancer. • This meta-analysis revealed that multiparametric ultrasound has moderate diagnostic accuracy for prostate cancer. • The diagnostic accuracy of multiparametric ultrasound in the diagnosis of clinically significant prostate cancer is significantly lower than any prostate cancer.
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Affiliation(s)
- Yun Tang
- Department of Geriatric Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
- Longmen Hao Street Community Health Service Center, Nan'an District, Chongqing, 401336, China
| | - Xingsheng Li
- Department of Geriatric Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
| | - Lingyun Zhai
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
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12
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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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13
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Hu X, Sun C, Ren X, Ge S, Xie C, Li X, Zhu Y, Ding H. Contrast-enhanced Ultrasound Combined With Elastography for the Evaluation of Muscle-invasive Bladder Cancer in Rats. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:1999-2011. [PMID: 36896871 DOI: 10.1002/jum.16216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES By comparing with the control group, we evaluated the usefulness of contrast-enhanced ultrasound (CEUS) combined with elastography for the assessment of muscle invasion by bladder cancer (MIBC) in a Sprague-Dawley (SD) rat model. METHODS In the experimental group, 40 SD rats developed in situ bladder cancer (BLCA) in response to N-methyl-N-nitrosourea treatment, whereas 40 SD rats were included in the control group for comparison. We compared PI, Emean , microvessel density (MVD), and collagen fiber content (CFC) between the two groups. In the experimental group, Bland-Altman test was used to assess the relationships between various parameters. The largest Youden value was used as the cut-off point, and binomial logistic regression analysis was performed to analyze the PI and Emean . Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic power of parameters, individually and in combination. RESULTS The PI, Emean , MVD, and CFC were significantly lower in the control group than in the experimental group (P < .05). The PI, Emean , MVD, and CFC were significantly higher for MIBC than for non-muscle-invasive bladder cancer (P < .05). There were significant correlations between PI and MVD, and between Emean and CFC. The diagnostic efficiency analysis showed PI had the highest sensitivity, CFC had the highest specificity, and PI + Emean had the highest diagnostic efficacy. CONCLUSION CEUS and elastography can distinguish lesions from normal tissue. PI, MVD, Emean , and CFC were useful for the detection of BLCA myometrial invasion. The comprehensive utilization of PI and Emean improved diagnostic accuracy and have clinical application.
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Affiliation(s)
- Xing Hu
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuanyu Sun
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinping Ren
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Shengyang Ge
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chunmei Xie
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiangyu Li
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yingfeng Zhu
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hong Ding
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
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14
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Wilson PFR, Gilany M, Jamzad A, Fooladgar F, To MNN, Wodlinger B, Abolmaesumi P, Mousavi P. Self-Supervised Learning With Limited Labeled Data for Prostate Cancer Detection in High-Frequency Ultrasound. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2023; 70:1073-1083. [PMID: 37478033 DOI: 10.1109/tuffc.2023.3297840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer (PCa) detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning (SL) paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of SL methods. However, unlabeled prostate ultrasound data are more abundant. In this work, we successfully apply self-supervised representation learning to micro-ultrasound data. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained in two clinical centers, we demonstrate that feature representations learned with this method can be used to classify cancer from noncancer tissue, obtaining an AUROC score of 91% on an independent test set. To the best of our knowledge, this is the first successful end-to-end self-SL (SSL) approach for PCa detection using ultrasound data. Our method outperforms baseline SL approaches, generalizes well between different data centers, and scales well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data. Our code is publicly available at https://www.github.com/MahdiGilany/SSL_micro_ultrasound.
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15
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Froń A, Semianiuk A, Lazuk U, Ptaszkowski K, Siennicka A, Lemiński A, Krajewski W, Szydełko T, Małkiewicz B. Artificial Intelligence in Urooncology: What We Have and What We Expect. Cancers (Basel) 2023; 15:4282. [PMID: 37686558 PMCID: PMC10486651 DOI: 10.3390/cancers15174282] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
INTRODUCTION Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. METHODOLOGY We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. RESULTS Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. CONCLUSIONS AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.
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Affiliation(s)
- Anita Froń
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Alina Semianiuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Uladzimir Lazuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Kuba Ptaszkowski
- Department of Physiotherapy, Wroclaw Medical University, 50-368 Wroclaw, Poland;
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-556 Wroclaw, Poland;
| | - Artur Lemiński
- Department of Urology and Urological Oncology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Wojciech Krajewski
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Tomasz Szydełko
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Bartosz Małkiewicz
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
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16
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Jager A, Postema AW, van der Linden H, Nooijen PTGA, Bekers E, Kweldam CF, Daures G, Zwart W, Mischi M, Beerlage HP, Oddens JR. Reliability of whole mount radical prostatectomy histopathology as the ground truth for artificial intelligence assisted prostate imaging. Virchows Arch 2023; 483:197-206. [PMID: 37407736 PMCID: PMC10412486 DOI: 10.1007/s00428-023-03589-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/05/2023] [Accepted: 06/26/2023] [Indexed: 07/07/2023]
Abstract
The development of artificial intelligence-based imaging techniques for prostate cancer (PCa) detection and diagnosis requires a reliable ground truth, which is generally based on histopathology from radical prostatectomy specimens. This study proposes a comprehensive protocol for the annotation of prostatectomy pathology slides. To evaluate the reliability of the protocol, interobserver variability was assessed between five pathologists, who annotated ten radical prostatectomy specimens consisting of 74 whole mount pathology slides. Interobserver variability was assessed for both the localization and grading of PCa. The results indicate excellent overall agreement on the localization of PCa (Gleason pattern ≥ 3) and clinically significant PCa (Gleason pattern ≥ 4), with Dice similarity coefficients (DSC) of 0.91 and 0.88, respectively. On a per-slide level, agreement for primary and secondary Gleason pattern was almost perfect and substantial, with Fleiss Kappa of .819 (95% CI .659-.980) and .726 (95% CI .573-.878), respectively. Agreement on International Society of Urological Pathology Grade Group was evaluated for the index lesions and showed agreement in 70% of cases, with a mean DSC of 0.92 for all index lesions. These findings show that a standardized protocol for prostatectomy pathology annotation provides reliable data on PCa localization and grading, with relatively high levels of interobserver agreement. More complicated tissue characterization, such as the presence of cribriform growth and intraductal carcinoma, remains a source of interobserver variability and should be treated with care when used in ground truth datasets.
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Affiliation(s)
- Auke Jager
- Amsterdam UMC, University of Amsterdam, Department of Urology, Meibergdreef 9, Amsterdam, The Netherlands.
| | - Arnoud W Postema
- Amsterdam UMC, University of Amsterdam, Department of Urology, Meibergdreef 9, Amsterdam, The Netherlands
- Department of Urology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Hans van der Linden
- Pathology DNA, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223, GZ, 's-Hertogenbosch, The Netherlands
| | - Peet T G A Nooijen
- Pathology DNA, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223, GZ, 's-Hertogenbosch, The Netherlands
| | - Elise Bekers
- Department of Pathology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | | | - Gautier Daures
- Angiogenesis Analytics, JADS Venture Campus, 's-Hertogenbosch, AA, The Netherlands
| | - Wim Zwart
- Angiogenesis Analytics, JADS Venture Campus, 's-Hertogenbosch, AA, The Netherlands
| | - M Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Harrie P Beerlage
- Amsterdam UMC, University of Amsterdam, Department of Urology, Meibergdreef 9, Amsterdam, The Netherlands
| | - Jorg R Oddens
- Amsterdam UMC, University of Amsterdam, Department of Urology, Meibergdreef 9, Amsterdam, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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17
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Zhang XY, Wei Q, Wu GG, Tang Q, Pan XF, Chen GQ, Zhang D, Dietrich CF, Cui XW. Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review. Front Oncol 2023; 13:1197447. [PMID: 37333814 PMCID: PMC10272784 DOI: 10.3389/fonc.2023.1197447] [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: 03/31/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023] Open
Abstract
Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.
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Affiliation(s)
- Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wei
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ge-Ge Wu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Tang
- Department of Ultrasonography, The First Hospital of Changsha, Changsha, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Di Zhang
- Department of Medical Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | | | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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18
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Sun YK, Zhou BY, Miao Y, Shi YL, Xu SH, Wu DM, Zhang L, Xu G, Wu TF, Wang LF, Yin HH, Ye X, Lu D, Han H, Xiang LH, Zhu XX, Zhao CK, Xu HX. Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study. EClinicalMedicine 2023; 60:102027. [PMID: 37333662 PMCID: PMC10276260 DOI: 10.1016/j.eclinm.2023.102027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/22/2023] [Accepted: 05/12/2023] [Indexed: 06/20/2023] Open
Abstract
Background Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa. Methods Between January 2021 and December 2022, this study prospectively evaluated 832 patients from four centres who underwent prostate biopsy and/or radical prostatectomy. All patients had a standardised TRUS video of the whole prostate. A two-dimensional CNN (2D P-Net) and three-dimensional CNN (3D P-Net) were constructed using the training cohort (559 patients) and tested on the internal validation cohort (140 patients) as well as on the external validation cohort (133 patients). The performance of 2D P-Net and 3D P-Net in predicting csPCa was assessed in terms of the area under the receiver operating characteristic curve (AUC), biopsy rate, and unnecessary biopsy rate, and compared with the TRUS 5-point Likert score system as well as multiparametric magnetic resonance imaging (mp-MRI) prostate imaging reporting and data system (PI-RADS) v2.1. Decision curve analyses (DCAs) were used to determine the net benefits associated with their use. The study is registered at https://www.chictr.org.cn with the unique identifier ChiCTR2200064545. Findings The diagnostic performance of 3D P-Net (AUC: 0.85-0.89) was superior to TRUS 5-point Likert score system (AUC: 0.71-0.78, P = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, P = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, P = 0.066-0.678) in the internal and external validation cohorts. The biopsy rate decreased from 40.3% (TRUS 5-point Likert score system) and 47.6% (mp-MRI PI-RADS v2.1 score system) to 35.5% (2D P-Net) and 34.0% (3D P-Net). The unnecessary biopsy rate decreased from 38.1% (TRUS 5-point Likert score system) and 35.2% (mp-MRI PI-RADS v2.1 score system) to 32.0% (2D P-Net) and 25.8% (3D P-Net). 3D P-Net yielded the highest net benefit according to the DCAs. Interpretation 3D P-Net based on a prostate grayscale TRUS video achieved satisfactory performance in identifying csPCa and potentially reducing unnecessary biopsies. More studies to determine how AI models better integrate into routine practice and randomized controlled trials to show the values of these models in real clinical applications are warranted. Funding The National Natural Science Foundation of China (Grants 82202174 and 82202153), the Science and Technology Commission of Shanghai Municipality (Grants 18441905500 and 19DZ2251100), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), Shanghai Science and Technology Innovation Action Plan (21Y11911200), and Fundamental Research Funds for the Central Universities (ZD-11-202151), Scientific Research and Development Fund of Zhongshan Hospital of Fudan University (Grant 2022ZSQD07).
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Affiliation(s)
- Yi-Kang Sun
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Bo-Yang Zhou
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Yao Miao
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumour, Shanghai Tenth People's Hospital, Ultrasound Institute of Research and Education, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Yi-Lei Shi
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Shi-Hao Xu
- Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Dao-Ming Wu
- Department of Ultrasound, Fujian Provincial Hospital, Fujian, China
| | - Lei Zhang
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Guang Xu
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumour, Shanghai Tenth People's Hospital, Ultrasound Institute of Research and Education, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Ting-Fan Wu
- Bayer Healthcare, Radiology, Shanghai, China
| | - Li-Fan Wang
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hao-Hao Yin
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Xin Ye
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Dan Lu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hong Han
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Li-Hua Xiang
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumour, Shanghai Tenth People's Hospital, Ultrasound Institute of Research and Education, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Xiao-Xiang Zhu
- Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany
| | - Chong-Ke Zhao
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
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Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound. EUR UROL SUPPL 2023; 49:32-43. [PMID: 36874606 PMCID: PMC9975006 DOI: 10.1016/j.euros.2022.12.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2022] [Indexed: 01/27/2023] Open
Abstract
Introduction and hypothesis The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately. Design This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs). Protocol overview Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported. Statistical analysis The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors. Discussion and summary This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa.
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20
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Chu TN, Wong EY, Ma R, Yang CH, Dalieh IS, Hung AJ. Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer. Curr Urol Rep 2023; 24:231-240. [PMID: 36808595 PMCID: PMC10090000 DOI: 10.1007/s11934-023-01149-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2023] [Indexed: 02/21/2023]
Abstract
PURPOSE OF REVIEW This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management. RECENT FINDINGS Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.
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Affiliation(s)
- Timothy N Chu
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Elyssa Y Wong
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Runzhuo Ma
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Cherine H Yang
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Istabraq S Dalieh
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA
| | - Andrew J Hung
- Center for Robotic Simulation & Education, Department of Urology, USC Institute of Urology, University of Southern California, Catherine & Joseph Aresty1441 Eastlake Avenue Suite 7416, Los Angeles, CA, 90089, USA.
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Paskali F, Simantzik J, Dieterich A, Kohl M. Specification of Neck Muscle Dysfunction through Digital Image Analysis Using Machine Learning. Diagnostics (Basel) 2022; 13:diagnostics13010007. [PMID: 36611299 PMCID: PMC9818408 DOI: 10.3390/diagnostics13010007] [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: 11/16/2022] [Revised: 12/17/2022] [Accepted: 12/18/2022] [Indexed: 12/24/2022] Open
Abstract
Everyone has or will have experienced some degree of neck pain. Typically, neck pain is associated with the sensation of tense, tight, or stiff neck muscles. However, it is unclear whether the neck muscles are objectively stiffer with neck pain. This study used 1099 ultrasound elastography images (elastograms) obtained from 38 adult women, 20 with chronic neck pain and 18 asymptomatic. For training machine learning algorithms, 28 numerical characteristics were extracted from both the original and transformed shear wave velocity color-coded images as well as from respective image segments. Overall, a total number of 323 distinct features were generated from the data. A supervised binary classification was performed, using six machine-learning algorithms. The random forest algorithm produced the most accurate model to distinguish the elastograms of women with chronic neck pain from asymptomatic women with an AUC of 0.898. When evaluating features that can be used as biomarkers for muscle dysfunction in neck pain, the region of the deepest neck muscles (M. multifidus) provided the most features to support the correct classification of elastograms. By constructing summary images and associated Hotelling's T2 maps, we enabled the visualization of group differences and their statistical confirmation.
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Affiliation(s)
- Filip Paskali
- Institute of Precision Medicine, Medical and Life Sciences, Hochschule Furtwangen, 78054 Villingen-Schwenningen, Germany
| | - Jonathan Simantzik
- Institute of Precision Medicine, Medical and Life Sciences, Hochschule Furtwangen, 78054 Villingen-Schwenningen, Germany
| | - Angela Dieterich
- Physiotherapie, Fakultät Gesundheit, Sicherheit, Gesellschaft, Hochschule Furtwangen, Studienzentrum Freiburg, 79110 Freiburg, Germany
| | - Matthias Kohl
- Institute of Precision Medicine, Medical and Life Sciences, Hochschule Furtwangen, 78054 Villingen-Schwenningen, Germany
- Correspondence:
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22
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Sigle A, Michaelis J, Schöb D, Benndorf M, Schimmöller L, Becker B, Pallauf M, Gross AJ, Herrmann TRW, Klein JT, Lusuardi L, Netsch C, Häcker A, Westphal J, Jilg C, Gratzke C, Miernik A. [Image-guided biopsy of the prostate gland]. UROLOGIE (HEIDELBERG, GERMANY) 2022; 61:1137-1148. [PMID: 36040512 DOI: 10.1007/s00120-022-01929-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
The recommendations on carrying out a multiparametric magnetic resonance imaging (mpMRI) for the primary diagnostics and during active surveillance of prostate cancer, include as a consequence an image-guided sampling from conspicuous areas. In doing so, the information on the localization provided by mpMRI is used for a targeted biopsy of the area suspected of being a tumor. The targeted sampling is mainly performed under sonographic control and after fusion of MRI and ultrasound but can also be (mostly in special cases) carried out directly in the MRI scanner. In an ultrasound-guided biopsy, it is vital to coregister the MR images with the ultrasound images (segmentation of the contour of the prostate and registration of suspect findings). This coregistration can either be carried out cognitively (transfer by the person performing the biopsy alone) or software based. Each method shows specific advantages and disadvantages in the prioritization between diagnostic accuracy and resource expenditure.
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Affiliation(s)
- August Sigle
- Medizinische Fakultät, Klinik für Urologie, Universitätsklinikum Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland.
- Berta-Ottenstein-Programm, Medizinische Fakultät, Universität Freiburg, Freiburg, Deutschland.
| | - Jakob Michaelis
- Medizinische Fakultät, Klinik für Urologie, Universitätsklinikum Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
| | - Dominik Schöb
- Medizinische Fakultät, Klinik für Urologie, Universitätsklinikum Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
| | - Matthias Benndorf
- Medizinische Fakultät, Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Freiburg, Deutschland
| | - Lars Schimmöller
- Medizinische Fakultät, Institut für Diagnostische und Interventionelle Radiologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Benedikt Becker
- Abteilung für Urologie, Asklepios Klinik Barmbek, Hamburg, Deutschland
| | - Maximilian Pallauf
- Johns Hopkins University, Baltimore, USA
- Department für Urologie und Onkologie, Paracelsus Medizinische Privatuniversität, Salzburg, Österreich
- Department für Urologie, Uniklinikum Salzburg, Salzburg, Österreich
| | - Andreas J Gross
- Abteilung für Urologie, Asklepios Klinik Barmbek, Hamburg, Deutschland
| | - Thomas R W Herrmann
- Urologie, Spital Thurgau AG, Frauenfeld, Schweiz
- Medizinische Hochschule Hannover, Hannover, Deutschland
- Division of Urology, Department of Surgical Sciences, Stellenbosch University, Western Cape, Südafrika
| | - Jan-Thorsten Klein
- Klinik für Urologie und Kinderurologie, Universitätsklinikum Ulm, Ulm, Deutschland
| | - Lukas Lusuardi
- Paracelsus Medizinische Universitätsklinik für Urologie, Salzburger Landeskliniken, Salzburg, Österreich
| | | | - Axel Häcker
- Klinik für Urologie, Universitätsklinikum Mannheim, Mannheim, Deutschland
| | - Jens Westphal
- Klinik für Urologie, Kinderurologie und Urogynäkologie, Krankenhaus Maria-Hilf, Akademisches Lehrkrankenhaus der Heinrich-Heine-Universität Düsseldorf, Krefeld, Deutschland
| | - Cordula Jilg
- Medizinische Fakultät, Klinik für Urologie, Universitätsklinikum Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
| | - Christian Gratzke
- Medizinische Fakultät, Klinik für Urologie, Universitätsklinikum Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
| | - Arkadiusz Miernik
- Medizinische Fakultät, Klinik für Urologie, Universitätsklinikum Freiburg, Hugstetter Straße 55, 79106, Freiburg, Deutschland
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23
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Liu Y, Wang S, Xiang LH, Xu G, Dong L, Sun Y, Ye B, Zhang Y, Xu H. The potential of a nomogram combined PI-RADS v2.1 and contrast-enhanced ultrasound (CEUS) to reduce unnecessary biopsies in prostate cancer diagnostics. Br J Radiol 2022; 95:20220209. [PMID: 35877385 PMCID: PMC9815734 DOI: 10.1259/bjr.20220209] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/20/2022] [Accepted: 07/18/2022] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVES To develop a nomogram prediction model based on Prostate Imaging Reporting and Data System v.2.1 (PI-RADS v2.1) and contrast-enhanced ultrasound (CEUS) for predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in males with prostate-specific antigen (PSA) 4-10 ng ml-1 to avoid unnecessary biopsy. METHODS A total of 490 patients who underwent prostate biopsy for PSA 4-10 ng ml-1 were enrolled and randomly divided into a pilot cohort (70%) and a validation cohort (30%). Univariate and multivariate logistic regression models were constructed to select potential predictors of PCa and csPCa, and a nomogram was created. The area under receiver operating characteristic (ROC) curve (AUC) was calculated, and compared using DeLong's test. The diagnostic performance and unnecessary biopsy rate of the nomogram prediction model were also assessed. Hosmer-Lemeshow goodness-of-fit test was employed to test for model fitness. RESULTS The multivariate analysis revealed that features independently associated with PCa and csPCa were age, PI-RADS score and CEUS manifestations. Incorporating these factors, the nomogram achieved good discrimination performance of AUC 0.843 for PCa, 0.876 for csPCa in the pilot cohort, and 0.818 for PCa, 0.857 for csPCa in the validation cohort, respectively, and had well-fitted calibration curves. And the diagnostic performance of the nomogram was comparable to the model including all the parameters (p > 0.05). Besides, the nomogram prediction model yielded meaningful reduction in unnecessary biopsy rate (from 74.8 to 21.1% in PCa, and from 83.7 to 5.4% in csPCa). CONCLUSIONS The nomogram prediction model based on age, PI-RADS v2.1 and CEUS achieved an optimal prediction of PCa and csPCa. Using this model, the PCa risk for an individual patient can be estimated, which can lead to a rational biopsy choice. ADVANCES IN KNOWLEDGE This study gives an account of improving pre-biopsy risk stratification in males with "gray zone" PSA level through PI-RADS v2.1 and CEUS.
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Jung N, DiNatale RG, Frankel J, Koenig H, Ho O, Flores JP, Porter C. The role of multiparametric ultrasound in the detection of clinically significant prostate cancer. World J Urol 2022; 41:663-671. [PMID: 35932319 DOI: 10.1007/s00345-022-04122-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 07/23/2022] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Transrectal ultrasound (US) imaging is paramount to the successful completion of prostate biopsies. Certain US features have been associated with prostate cancer (PCa), but their utility remains controversial. We explored the role of multiparametric US (mpUS) in the detection of clinically significant PCa. METHODS We performed a retrospective cohort study to contrast the findings of prostate MRI and mpUS. Patients who underwent MRI, US and biopsy between 2015 and 2021 were included. Biopsies were performed using a systematic approach (12 cores), as well as with MRI (4 cores/lesion) and US (1 core/lesion) targeting. The US features analyzed consisted of: calcifications, hypoechoic lesions and power or color Doppler positivity. Gleason 3 + 4 or higher was used as to define true positives. Measures of diagnostic accuracy were calculated for the different imaging modalities. RESULTS The final cohort included 74 patients, of which 24 (32.4%) had clinically significant PCa. The concordance between MRI and US was 63.5%. Seven individuals with discordant results had clinically significant PCa. MRI alone was more sensitive (87.5% vs 75%) but less specific (28% vs 32%) than US alone. An all-inclusive approach considering any suspicious US or MRI finding had a sensitivity of 95.8%. A more restrictive approach, targeting lesions noted in both US and MRI, yielded the highest specificity (50.0%) and accuracy (55.4%). CONCLUSION Biopsy targeting based on US findings can provide additional diagnostic information that may increase sensitivity or specificity. Additional research into this topic could open the door to a more personalized approach to prostate biopsy.
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Affiliation(s)
- Nathan Jung
- Urology and Renal Transplantation Service, Department of Surgery, Virginia Mason Medical Center, 1100 Ninth Ave., Seattle, WA, 98101, USA
| | - Renzo G DiNatale
- Urology and Renal Transplantation Service, Department of Surgery, Virginia Mason Medical Center, 1100 Ninth Ave., Seattle, WA, 98101, USA
| | - Jason Frankel
- Urology, SLU Care Urology, 6400 Clayton Rd, Clayton, MO, 63177, USA
| | - Hannah Koenig
- Urology and Renal Transplantation Service, Department of Surgery, Virginia Mason Medical Center, 1100 Ninth Ave., Seattle, WA, 98101, USA
| | - On Ho
- Urology and Renal Transplantation Service, Department of Surgery, Virginia Mason Medical Center, 1100 Ninth Ave., Seattle, WA, 98101, USA
| | - John Paul Flores
- Hematology and Oncology Service, Department of Medicine, Virginia Mason Medical Center, 1100 Ninth Ave., Seattle, WA, 98101, USA
| | - Christopher Porter
- Urology and Renal Transplantation Service, Department of Surgery, Virginia Mason Medical Center, 1100 Ninth Ave., Seattle, WA, 98101, USA.
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Li H, Bhatt M, Qu Z, Zhang S, Hartel MC, Khademhosseini A, Cloutier G. Deep learning in ultrasound elastography imaging: A review. Med Phys 2022; 49:5993-6018. [PMID: 35842833 DOI: 10.1002/mp.15856] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 02/04/2022] [Accepted: 07/06/2022] [Indexed: 11/11/2022] Open
Abstract
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hongliang Li
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada
| | - Manish Bhatt
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Zhen Qu
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Shiming Zhang
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Martin C Hartel
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Ali Khademhosseini
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Guy Cloutier
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada.,Department of Radiology, Radio-Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada
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Wei X, Wang Y, Ge L, Peng B, He Q, Wang R, Huang L, Xu Y, Luo J. Unsupervised Convolutional Neural Network for Motion Estimation in Ultrasound Elastography. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:2236-2247. [PMID: 35500076 DOI: 10.1109/tuffc.2022.3171676] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
High-quality motion estimation is essential for ultrasound elastography (USE). Traditional motion estimation algorithms based on speckle tracking such as normalized cross correlation (NCC) or regularization such as global ultrasound elastography (GLUE) are time-consuming. In order to reduce the computational cost and ensure the accuracy of motion estimation, many convolutional neural networks have been introduced into USE. Most of these networks such as radio-frequency modified pyramid, warping and cost volume network (RFMPWC-Net) are supervised and need many ground truths as labels in network training. However, the ground truths are laborious to collect for USE. In this study, we proposed a MaskFlownet-based unsupervised convolutional neural network (MF-UCNN) for fast and high-quality motion estimation in USE. The inputs to MF-UCNN are the concatenation of RF, envelope, and B-mode images before and after deformation, while the outputs are the axial and lateral displacement fields. The similarity between the predeformed image and the warped image (i.e., the postdeformed image compensated by the estimated displacement fields) and the smoothness of the estimated displacement fields were incorporated in the loss function. The network was compared with modified pyramid, warping and cost volume network (MPWC-Net)++, RFMPWC-Net, GLUE, and NCC. Results of simulations, breast phantom, and in vivo experiments show that MF-UCNN obtains higher signal-to-noise ratio (SNR) and higher contrast-to-noise ratio (CNR). MF-UCNN achieves high-quality motion estimation with significantly reduced computation time. It is unsupervised and does not need any ground truths as labels in the training, and, thus, has great potential for motion estimation in USE.
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Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer. Metabolites 2022; 12:metabo12050409. [PMID: 35629913 PMCID: PMC9145477 DOI: 10.3390/metabo12050409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/16/2022] Open
Abstract
Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5−3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.
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Gurwin A, Kowalczyk K, Knecht-Gurwin K, Stelmach P, Nowak Ł, Krajewski W, Szydełko T, Małkiewicz B. Alternatives for MRI in Prostate Cancer Diagnostics-Review of Current Ultrasound-Based Techniques. Cancers (Basel) 2022; 14:1859. [PMID: 35454767 PMCID: PMC9028694 DOI: 10.3390/cancers14081859] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/01/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
The purpose of this review is to present the current role of ultrasound-based techniques in the diagnostic pathway of prostate cancer (PCa). With overdiagnosis and overtreatment of a clinically insignificant PCa over the past years, multiparametric magnetic resonance imaging (mpMRI) started to be recommended for every patient suspected of PCa before performing a biopsy. It enabled targeted sampling of the suspicious prostate regions, improving the accuracy of the traditional systematic biopsy. However, mpMRI is associated with high costs, relatively low availability, long and separate procedure, or exposure to the contrast agent. The novel ultrasound modalities, such as shear wave elastography (SWE), contrast-enhanced ultrasound (CEUS), or high frequency micro-ultrasound (MicroUS), may be capable of maintaining the performance of mpMRI without its limitations. Moreover, the real-time lesion visualization during biopsy would significantly simplify the diagnostic process. Another value of these new techniques is the ability to enhance the performance of mpMRI by creating the image fusion of multiple modalities. Such models might be further analyzed by artificial intelligence to mark the regions of interest for investigators and help to decide about the biopsy indications. The dynamic development and promising results of new ultrasound-based techniques should encourage researchers to thoroughly study their utilization in prostate imaging.
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Affiliation(s)
- Adam Gurwin
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (K.K.); (P.S.); (Ł.N.); (W.K.); (T.S.)
| | - Kamil Kowalczyk
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (K.K.); (P.S.); (Ł.N.); (W.K.); (T.S.)
| | - Klaudia Knecht-Gurwin
- Department of Dermatology, Venereology and Allergology, Wroclaw Medical University, 50-368 Wroclaw, Poland;
| | - Paweł Stelmach
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (K.K.); (P.S.); (Ł.N.); (W.K.); (T.S.)
| | - Łukasz Nowak
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (K.K.); (P.S.); (Ł.N.); (W.K.); (T.S.)
| | - Wojciech Krajewski
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (K.K.); (P.S.); (Ł.N.); (W.K.); (T.S.)
| | - Tomasz Szydełko
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (K.K.); (P.S.); (Ł.N.); (W.K.); (T.S.)
| | - Bartosz Małkiewicz
- University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (K.K.); (P.S.); (Ł.N.); (W.K.); (T.S.)
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Apfelbeck M, Clevert DA, Stief CG, Chaloupka M. [Sonography of the prostate : Relevance for urologists in daily clinical routine]. Urologe A 2022; 61:365-373. [PMID: 35244746 DOI: 10.1007/s00120-022-01767-x] [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] [Accepted: 01/14/2022] [Indexed: 11/25/2022]
Abstract
Despite the continuous technical progress regarding the transrectal ultrasonography of the prostate (TRUS) and its successful use in combination with magnetic resonance imaging (MRI) in MRI-targeted biopsy, there is no radiologic modality being able to rule out clinically significant prostate cancer without the need of systematic biopsy. In the past few years, TRUS regained more attention due to the development of high frequency ultrasound as well as the combination of different ultrasonic modalities like shear wave elastography and contrast-enhanced sonography (CEUS). Currently, multiparametric MRI (mpMRI)-targeted biopsy shows the best results concerning detection rates, sensitivity and specificity of clinically significant prostate cancer compared to systematic biopsy. In the future, transperineal biopsy is probably going to increasingly replace the transrectal biopsy approach. For both approaches, transrectal ultrasonography is necessary to display the prostate and to detect suspicious lesions. Therefore future improvements in transrectal ultrasonography can be expected.
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Affiliation(s)
- Maria Apfelbeck
- Urologische Klinik und Poliklinik des LMU Klinikums, Campus Großhadern, Ludwig-Maximilians-Universität München, Marchioninistr. 15, 81377, München, Deutschland.
| | - Dirk-André Clevert
- Klinik und Poliklinik für Radiologie des LMU Klinikums, Campus Großhadern, Ludwig-Maximilians-Universität München, Marchioninistr. 15, 81377, München, Deutschland
| | - Christian G Stief
- Urologische Klinik und Poliklinik des LMU Klinikums, Campus Großhadern, Ludwig-Maximilians-Universität München, Marchioninistr. 15, 81377, München, Deutschland
| | - Michael Chaloupka
- Urologische Klinik und Poliklinik des LMU Klinikums, Campus Großhadern, Ludwig-Maximilians-Universität München, Marchioninistr. 15, 81377, München, Deutschland
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Qiu L, Zhang X, Mao H, Fang X, Ding W, Zhao L, Chen H. Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan. Front Oncol 2022; 11:691112. [PMID: 35059308 PMCID: PMC8765579 DOI: 10.3389/fonc.2021.691112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 12/02/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To investigative the diagnostic performance of the morphological model, radiomics model, and combined model in differentiating invasive adenocarcinomas (IACs) from minimally invasive adenocarcinomas (MIAs). Methods This study retrospectively involved 307 patients who underwent chest computed tomography (CT) examination and presented as subsolid pulmonary nodules whose pathological findings were MIAs or IACs from January 2010 to May 2018. These patients were randomly assigned to training and validation groups in a ratio of 4:1 for 10 times. Eighteen categories of morphological features of pulmonary nodules including internal and surrounding structure were labeled. The following radiomics features are extracted: first-order features, shape-based features, gray-level co-occurrence matrix (GLCM) features, gray-level size zone matrix (GLSZM) features, gray-level run length matrix (GLRLM) features, and gray-level dependence matrix (GLDM) features. The chi-square test and F1 test selected morphology features, and LASSO selected radiomics features. Logistic regression was used to establish models. Receiver operating characteristic (ROC) curves evaluated the effectiveness, and Delong analysis compared ROC statistic difference among three models. Results In validation cohorts, areas under the curve (AUC) of the morphological model, radiomics model, and combined model of distinguishing MIAs from IACs were 0.88, 0.87, and 0.89; the sensitivity (SE) was 0.68, 0.81, and 0.83; and the specificity (SP) was 0.93, 0.79, and 0.87. There was no statistically significant difference in AUC between three models (p > 0.05). Conclusion The morphological model, radiomics model, and combined model all have a high efficiency in the differentiation between MIAs and IACs and have potential to provide non-invasive assistant information for clinical decision-making.
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Affiliation(s)
- Lu Qiu
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China.,Department of Radiology, Wuxi Children's Hospital, Nanjing Medical University, Wuxi, China
| | - Xiuping Zhang
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Haixia Mao
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Xiangming Fang
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Wei Ding
- Department of Intervention, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
| | - Lun Zhao
- Department of Research and Development, Deepwise Medical Artificial Intelligence Research Institute, Beijing, China
| | - Hongwei Chen
- Department of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, China
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31
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Gu JH, He C, Zhao QY, Jiang TA. Usefulness of new shear wave elastography in early predicting the efficacy of neoadjuvant chemotherapy for patients with breast cancer: where and when to measure is optimal? Breast Cancer 2022; 29:478-486. [PMID: 35038129 DOI: 10.1007/s12282-021-01327-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 12/22/2021] [Indexed: 11/02/2022]
Abstract
BACKGROUND The aim of this study was to investigate the diagnosis performance of new shear wave elastography (sound touch elastography, STE) in the prediction of neoadjuvant chemotherapy (NAC) response at an early stage in breast cancer patients and to determine the optimal measurement locations around the lesion in different ranges. METHODS One hundred and eight patients were analyzed in this prospective study from November 2018 to December 2020. All patients completed NAC treatment and underwent STE examination at three time points [the day before NAC (t0); the day before the second course (t1); the day before third course (t2)]. The stiffness of the whole lesion (G), 1-mm shell (S1) and 2-mm shell (S2) around the lesion was expressed by STE parameters. The relative changes (∆stiffness) of STE parameters after the first and second course of NAC were calculated and shown as the variables [Δ(t1) and Δ(t2)]. The diagnostic accuracy of STE was evaluated by means of receiver operating characteristic curve analysis. RESULTS The ∆stiffness (%) including ∆Gmean(t2), ∆S1mean(t2) and ∆S2mean(t2) all showed significant differences between pathological complete response (pCR) and non-pCR groups. ∆S2mean(t2) displayed the best predictive performance for pCR (AUC = 0.842) with an ideal ∆stiffness threshold value - 26%. CONCLUSIONS Measuring the relative changes in the stiffness of surrounding tissue or entire lesion with STE holds promise for effectively predicting the response to NAC at its early stage for breast cancer patients and ∆stiffness of shell 2 mm after the second course of NAC may be a potential prediction parameter.
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Affiliation(s)
- Jiong-Hui Gu
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, People's Republic of China
| | - Chang He
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, People's Republic of China
| | - Qi-Yu Zhao
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, People's Republic of China
| | - Tian-An Jiang
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, People's Republic of China.
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Kaneko M, Lenon MSL, Storino Ramacciotti L, Medina LG, Sayegh AS, La Riva A, Perez LC, Ghoreifi A, Lizana M, Jadvar DS, Lebastchi AH, Cacciamani GE, Abreu AL. Multiparametric ultrasound of prostate: role in prostate cancer diagnosis. Ther Adv Urol 2022; 14:17562872221145625. [PMID: 36601020 PMCID: PMC9806443 DOI: 10.1177/17562872221145625] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 11/25/2022] [Indexed: 12/28/2022] Open
Abstract
Recent advances in ultrasonography (US) technology established modalities, such as Doppler-US, HistoScanning, contrast-enhanced ultrasonography (CEUS), elastography, and micro-ultrasound. The early results of these US modalities have been promising, although there are limitations including the need for specialized equipment, inconsistent results, lack of standardizations, and external validation. In this review, we identified studies evaluating multiparametric ultrasonography (mpUS), the combination of multiple US modalities, for prostate cancer (PCa) diagnosis. In the past 5 years, a growing number of studies have shown that use of mpUS resulted in high PCa and clinically significant prostate cancer (CSPCa) detection performance using radical prostatectomy histology as the reference standard. Recent studies have demonstrated the role mpUS in improving detection of CSPCa and guidance for prostate biopsy and therapy. Furthermore, some aspects including lower costs, real-time imaging, applicability for some patients who have contraindication for magnetic resonance imaging (MRI) and availability in the office setting are clear advantages of mpUS. Interobserver agreement of mpUS was overall low; however, this limitation can be improved using standardized and objective evaluation systems such as the machine learning model. Whether mpUS outperforms MRI is unclear. Multicenter randomized controlled trials directly comparing mpUS and multiparametric MRI are warranted.
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Affiliation(s)
- Masatomo Kaneko
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Urology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Maria Sarah L. Lenon
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lorenzo Storino Ramacciotti
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis G. Medina
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Aref S. Sayegh
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anibal La Riva
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Laura C. Perez
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alireza Ghoreifi
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Maria Lizana
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Donya S. Jadvar
- Dornsife School of Letters and Science, University of Southern California, Los Angeles, CA, USA
| | - Amir H. Lebastchi
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Giovanni E. Cacciamani
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andre Luis Abreu
- Center for Image-Guided Surgery, Focal Therapy, and Artificial Intelligence for Prostate Cancer, USC Institute of Urology and Catherine & Joseph Aresty
- Department of Urology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Suite 7416, Los Angeles, CA 90089, USADepartment of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Bhattacharya I, Khandwala YS, Vesal S, Shao W, Yang Q, Soerensen SJ, Fan RE, Ghanouni P, Kunder CA, Brooks JD, Hu Y, Rusu M, Sonn GA. A review of artificial intelligence in prostate cancer detection on imaging. Ther Adv Urol 2022; 14:17562872221128791. [PMID: 36249889 PMCID: PMC9554123 DOI: 10.1177/17562872221128791] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/30/2022] [Indexed: 11/07/2022] Open
Abstract
A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.
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Affiliation(s)
- Indrani Bhattacharya
- Department of Radiology, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA 94305, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yash S. Khandwala
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sulaiman Vesal
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wei Shao
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Qianye Yang
- Centre for Medical Image Computing, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Simon J.C. Soerensen
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Richard E. Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Pejman Ghanouni
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian A. Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - James D. Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Mirabela Rusu
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Geoffrey A. Sonn
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
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Huang J, Lu Y, Wang X, Zhu X, Li P, Chen J, Chen P, Ding M. Diagnostic value of endobronchial ultrasound elastography combined with rapid onsite cytological evaluation in endobronchial ultrasound-guided transbronchial needle aspiration. BMC Pulm Med 2021; 21:423. [PMID: 34930196 PMCID: PMC8690901 DOI: 10.1186/s12890-021-01748-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 11/11/2021] [Indexed: 11/16/2022] Open
Abstract
Background Endobronchial ultrasound (EBUS) elastography has been used in EBUS-guided transbronchial needle aspiration (EBUS-TBNA) to identify malignant lymph nodes based on tissue stiffness. Rapid onsite cytological evaluation (ROSE) has been widely utilized for onsite evaluation of sample adequacy and for guiding sampling during EBUS-TBNA. The aim of this study was to investigate the diagnostic value of combined EBUS elastography and ROSE in evaluating mediastinal and hilar lymph node status. Methods Retrospective chart review was performed from December 2018 to September 2020. Patient demographics, EBUS elastography scores, and ROSE, pathologic, and clinical outcome data were collected. The EBUS elastography scores were classified as follows: Type 1, predominantly nonblue; Type 2, partially blue and partially nonblue; and Type 3, predominantly blue. A receiver operating characteristic curve was used to compare the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for evaluation of malignant lymph nodes among the EBUS elastography, ROSE, and EBUS combined with ROSE groups. Results A total of 245 patients (345 lymph nodes) were included. The sensitivity and specificity of the EBUS elastography group for the diagnosis of malignant lymph nodes were 90.51% and 57.26%, respectively. The sensitivity and specificity in the ROSE group were 96.32% and 79.05%, respectively. The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of EBUS elastography combined with ROSE were 86.61%, 92.65%, 11.78, and 0.14, respectively, and the area under the curve was 0.942. Conclusions Combining EBUS elastography and ROSE significantly increased the diagnostic value of EBUS-TBNA in evaluating mediastinal and hilar lymph node status compared to each method alone. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-021-01748-4.
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Affiliation(s)
- Jing Huang
- Department of Respiratory and Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China
| | - Yuan Lu
- Department of Respiratory and Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China
| | - Xihua Wang
- Department of Respiratory and Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China
| | - Xiaoli Zhu
- Department of Respiratory and Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China
| | - Ping Li
- Department of Respiratory and Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China
| | - Jing Chen
- Department of Respiratory and Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China
| | - Pingsheng Chen
- Department of pathology and pathophysiology, School of Medicine, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China.
| | - Ming Ding
- Department of Respiratory and Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Dingjiaqiao 87#, Nanjing, Jiangsu, China.
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Rai BP, Mayerhofer C, Somani BK, Kallidonis P, Nagele U, Tokas T. Magnetic Resonance Imaging/Ultrasound Fusion-guided Transperineal Versus Magnetic Resonance Imaging/Ultrasound Fusion-guided Transrectal Prostate Biopsy-A Systematic Review. Eur Urol Oncol 2021; 4:904-913. [PMID: 33478936 DOI: 10.1016/j.euo.2020.12.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/25/2020] [Accepted: 12/20/2020] [Indexed: 11/23/2022]
Abstract
CONTEXT Magnetic resonance imaging (MRI)-targeted biopsies have changed the dogma in prostate cancer diagnosis. Biopsies can be performed either transrectally (MRI-guided and transrectal ultrasound fusion transrectal biopsy [MRI-TRUSB]) or transperineally (MRI-guided and transrectal ultrasound fusion transperineal biopsy [MRI-TPB]). OBJECTIVE To evaluate the detection and complication rates of MRI-TRUSB and MRI-TPB. EVIDENCE ACQUISITION We performed a literature search in PubMed, Scopus, EMBASE, and CENTRAL, and selected randomized controlled trials (RCTs) and observational studies comparing MRI-TRUSB versus MRI-TPB. EVIDENCE SYNTHESIS Our search identified 3608 studies; we included five in the qualitative and two in the quantitative synthesis. On per-patient pooled analysis for clinically significant prostate cancer (csPCa), MRI-TPB detection rates were significantly higher (relative risk 1.28 [95% confidence interval {CI} 1.03-1.60], p = 0.03). On a per-lesion analysis, MRI-TPB anterior csPCa detection rates were statistically significantly higher (relative risk 2.46 [95% CI 1.22-4.98], p = 0.01). On a per-lesion analysis, MRI-TPB and MRI-TRUSB overall cancer detection rates were 75% and 81.6% (p= 0.53), and csPCa detection rates were 65.7% and 75.5% (p = 0.40), respectively. MRI-TPB had lower complication rates (odds ratio 2.56 [95% CI 1.14-5.56, p < 0.05]). On Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evaluation, we rated all outcomes as "very low" certainty of the evidence for all outcome measures. CONCLUSIONS This review highlights the paucity of good-quality evidence comparing MRI-TPB and MRI-TRUSB. MRI-TPB achieves better detection for csPCa, anterior tumors, and lower infective complications. While RCTs are the highest quality of evidence that can address existing evidence limitations, there are concerns regarding infective complications associated with the MRI-TRUSB. Therefore, the authors propose that researchers and clinicians adopt a pragmatic approach by maintaining prospective databases, internal auditing of the MRI-TPB approach, and comparing these data with historical MRI-TRUSB cohorts. PATIENT SUMMARY We looked at the outcomes by comparing magnetic resonance imaging (MRI)-guided and transrectal ultrasound fusion transrectal biopsy with MRI-guided and transrectal ultrasound fusion transperineal biopsy (TPB). The analysis suggests, based on very low certainty evidence, that MRI-TPB has better detection for clinically significant prostate cancer, anterior tumors, and lower complications.
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Affiliation(s)
| | - Christoph Mayerhofer
- Department of Urology and Andrology, General Hospital Hall i.T., Hall in Tirol, Austria; Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group
| | - Bhaskar Kumar Somani
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
| | | | - Udo Nagele
- Department of Urology and Andrology, General Hospital Hall i.T., Hall in Tirol, Austria; Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group
| | - Theodoros Tokas
- Department of Urology and Andrology, General Hospital Hall i.T., Hall in Tirol, Austria; Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group.
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Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, Scifo P, Picchio M. State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol 2021; 169:103544. [PMID: 34801699 DOI: 10.1016/j.critrevonc.2021.103544] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/18/2021] [Accepted: 10/18/2021] [Indexed: 02/04/2023] Open
Abstract
We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.
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Affiliation(s)
| | | | - Luca Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentino Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annarita Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ilaria Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Erik Preza
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy; Radiology Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021; 22:ijms22189971. [PMID: 34576134 PMCID: PMC8465891 DOI: 10.3390/ijms22189971] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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Komatsu M, Sakai A, Dozen A, Shozu K, Yasutomi S, Machino H, Asada K, Kaneko S, Hamamoto R. Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines 2021; 9:720. [PMID: 34201827 PMCID: PMC8301304 DOI: 10.3390/biomedicines9070720] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/13/2021] [Accepted: 06/18/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.
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Affiliation(s)
- Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Akira Sakai
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel) 2021; 11:742. [PMID: 33919342 PMCID: PMC8143297 DOI: 10.3390/diagnostics11050742] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 02/06/2023] Open
Abstract
The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.
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Affiliation(s)
- Rima Hajjo
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan;
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA;
- National Center for Epidemics and Communicable Disease Control, Amman 11118, Jordan
| | - Dima A. Sabbah
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan;
| | - Sanaa K. Bardaweel
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Jordan, Amman 11942, Jordan;
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA;
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Liang L, Zhi X, Sun Y, Li H, Wang J, Xu J, Guo J. A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions. Front Oncol 2021; 11:610785. [PMID: 33738255 PMCID: PMC7962672 DOI: 10.3389/fonc.2021.610785] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 01/25/2021] [Indexed: 12/14/2022] Open
Abstract
Objectives To evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa). Methods A total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve. Results The multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model. Conclusions Clinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.
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Affiliation(s)
- Lei Liang
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Xin Zhi
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Ya Sun
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Huarong Li
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Jiajun Wang
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Jun Guo
- Department of Ultrasound, Aerospace Center Hospital, Beijing, China
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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Hameed BMZ, Shah M, Naik N, Ibrahim S, Somani B, Rice P, Soomro N, Rai BP. Contemporary application of artificial intelligence in prostate cancer: an i-TRUE study. Ther Adv Urol 2021; 13:1756287220986640. [PMID: 33633799 PMCID: PMC7841858 DOI: 10.1177/1756287220986640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 12/16/2020] [Indexed: 01/04/2023] Open
Abstract
Artificial intelligence (AI) involves technology that is able to emulate tasks previously carried out by humans. The growing incidence, novel diagnostic strategies and newer available therapeutic options have had resource and economic impacts on the healthcare organizations providing prostate cancer care. AI has the potential to be an adjunct to and, in certain cases, a replacement for human input in prostate cancer care delivery. Automation can also address issues such as inter- and intra-observer variability and has the ability to deliver analysis of large volume datasets quickly and accurately. The continuous training and testing of AI algorithms will facilitate development of futuristic AI models that will have integral roles to play in diagnostics, enhanced training and surgical outcomes and developments of prostate cancer predictive tools. These AI related innovations will enable clinicians to provide individualized care. Despite its potential benefits, it is vital that governance with AI related care is maintained and responsible adoption is achieved.
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Affiliation(s)
- B M Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Nithesh Naik
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Sufyan Ibrahim
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
| | - Bhaskar Somani
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Patrick Rice
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
| | - Naeem Soomro
- Department of Urology, Freeman Hospital, Newcastle, UK
| | - Bhavan Prasad Rai
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
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Anbarasan T, Wei C, Bamber JC, Barr RG, Nabi G. Characterisation of Prostate Lesions Using Transrectal Shear Wave Elastography (SWE) Ultrasound Imaging: A Systematic Review. Cancers (Basel) 2021; 13:122. [PMID: 33558449 PMCID: PMC7795187 DOI: 10.3390/cancers13010122] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/15/2020] [Accepted: 12/28/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND ultrasound-based shear wave elastography (SWE) can non-invasively assess prostate tissue stiffness. This systematic review aims to evaluate SWE for the detection of prostate cancer (PCa) and compare diagnostic estimates between studies reporting the detection of all PCa and clinically significant PCa (csPCa). METHODS a literature search was performed using the MEDLINE, EMBASE, Cochrane Library, ClinicalTrials.gov, and CINAHL databases. Studies evaluating SWE for the detection of PCa using histopathology as reference standard were included. RESULTS 16 studies including 2277 patients were included for review. Nine studies evaluated SWE for the detection of PCa using systematic biopsy as a reference standard at the per-sample level, with a pooled sensitivity and specificity of 0.85 (95% CI = 0.74-0.92) and 0.85 (95% CI = 0.75-0.91), respectively. Five studies evaluated SWE for the detection of PCa using histopathology of radical prostatectomy (RP) specimens as the reference standard, with a pooled sensitivity and specificity of 0.71 (95% CI = 0.55-0.83) and 0.74 (95% CI = 0.42-0.92), respectively. Sub-group analysis revealed a higher pooled sensitivity (0.77 vs. 0.62) and specificity (0.84 vs. 0.53) for detection of csPCa compared to all PCa among studies using RP specimens as the reference standard. CONCLUSION SWE is an attractive imaging modality for the detection of PCa.
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Affiliation(s)
- Thineskrishna Anbarasan
- College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Cheng Wei
- Academic Section of Urology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.W.); (G.N.)
| | - Jeffrey C. Bamber
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London SM2 5NG, UK;
| | - Richard G. Barr
- Department of Radiology, Northeastern Ohio Medical University, Rootstown, OH 44272, USA;
| | - Ghulam Nabi
- Academic Section of Urology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.W.); (G.N.)
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Della Pepa GM, Menna G, Stifano V, Pezzullo AM, Auricchio AM, Rapisarda A, Caccavella VM, La Rocca G, Sabatino G, Marchese E, Olivi A. Predicting meningioma consistency and brain-meningioma interface with intraoperative strain ultrasound elastography: a novel application to guide surgical strategy. Neurosurg Focus 2021; 50:E15. [PMID: 33386015 DOI: 10.3171/2020.10.focus20797] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/22/2020] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Providing new tools to improve surgical planning is considered a main goal in meningioma treatment. In this context, two factors are crucial in determining operating strategy: meningioma-brain interface and meningioma consistency. The use of intraoperative ultrasound (ioUS) elastosonography, a real-time imaging technique, has been introduced in general surgery to evaluate similar features in other pathological settings such as thyroid and prostate cancer. The aim of the present study was to evaluate ioUS elastosonography in the intraoperative prediction of key intracranial meningioma features and to evaluate its application in guiding surgical strategy. METHODS An institutional series of 36 meningiomas studied with ioUS elastosonography is reported. Elastographic data, intraoperative surgical findings, and corresponding preoperative MRI features were classified, applying a score from 0 to 2 to both meningioma consistency and meningioma-brain interface. Statistical analysis was performed to determine the degree of agreement between meningioma elastosonographic features and surgical findings, and whether intraoperative elastosonography was a better predictor than preoperative MRI in assessing meningioma consistency and slip-brain interface, using intraoperative findings as the gold standard. RESULTS A significantly high degree of reliability and agreement between ioUS elastographic scores and surgical finding scores was reported (intraclass correlation coefficient = 0.848, F = 12.147, p < 0.001). When analyzing both consistency and brain-tumor interface, ioUS elastography proved to have a rather elevated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive (LR+) and negative likelihood ratio (LR-). This consideration was true especially for meningiomas with a hard consistency (sensitivity = 0.92, specificity = 0.96, PPV = 0.92, NPV = 0.96, LR+ = 22.00, LR- = 0.09) and for those presenting with an adherent slip-brain interface (sensitivity = 0.76, specificity = 0.95, PPV = 0.93, NPV = 0.82, LR+ = 14.3, LR- = 0.25). Furthermore, predictions derived from ioUS elastography were found to be more accurate than MRI-derived predictions, as demonstrated by McNemar's test results in both consistency (p < 0.001) and interface (p < 0.001). CONCLUSIONS While external validation of the data is needed to transform ioUS elastography into a fully deployable clinical tool, this experience confirmed that it may be integrated into meningioma surgical planning, especially because of its rapidity and cost-effectiveness.
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Affiliation(s)
| | | | | | - Angelo Maria Pezzullo
- 2Public Health Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy
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Jung EM, Wertheimer T, Putz FJ, Jung F, Kammerer S, Pregler B, Luerken L, Stroszczynski C, Beyer L. Contrast enhanced ultrasound (CEUS) with parametric imaging and time intensity curve analysis (TIC) for evaluation of the success of prostate arterial embolization (PAE) in cases of prostate hyperplasia. Clin Hemorheol Microcirc 2020; 76:143-153. [PMID: 32925006 DOI: 10.3233/ch-209202] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AIM To evaluate the use of dynamic contrast enhanced ultrasound (CEUS) with parametric color-coded imaging and time intensity curve analysis (TIC) for planning and follow-up after prostate arterial embolization (PAE). MATERIAL/METHOD Before and after selective iliacal embolization by PAE with a follow up of 6 months 18 male patients (43-78 years, mean 63±3.5 years) with histopathological proven benign prostate hyperplasia were examined by one experienced examiner. A multifrequency high resolution probe (1-6 MHz) was used for transabdominal ultrasound and CEUS with bolus injections of 2.4 ml sulphur-hexafluoride microbubbles. Independent evaluation of color-coded parametric imaging before and after PAE by in PACS stored DICOM loops from arterial phase (10-15 s) up to 1min were performed. Criteria for successful treatment were reduction of early arterial enhancement by changes of time to peak (TTP) and area under the curve (AUC) by measurements in 8 regions of interest (ROI) of 5 mm in diameter at the margin and in the center and changes from hyperenhancement in parametric imaging (perfusion evaluation of arterial enhancement over 15 s) from red and yellow to blue and green by partial infarctions. Reference imaging method was the contrast high resolution 3 tesla magnetic resonance tomography (MRI) using 3D vibe sequences before and after PAE and for the follow up after 3 and 6 months. RESULTS PAE was technically and clinically successful in all 18 patients with less clinical symptoms and reduction of the gland volume. In all cases color-coded CEUS parametric imaging was able to evaluate partial infarction after embolization with changes from red and yellow to green and blue colors in the embolization areas. Relevant changes could be evaluated for TIC-analysis of CEUS with reduced arterial enhancement in the arterial phase and prolonged enhancement of up to 1 min with significant changes (p = 0.0024). The area under the curve (AUC) decreased from 676±255.04 rU (160 rU-1049 rU) before PAE to 370.43±255.19 rU (45 rU-858 rU) after PAE. Time to peak (TTP) did not change significantly (p = 0.6877); TTP before PAE was 25.82±9.04 s (12.3 s-42.5 s) and after PAE 24.43±9.10 s (12-39 s). Prostate volume decreased significantly (p = 0.0045) from 86.93±34.98 ml (30-139 ml) before PAE to 50.57±26.26 ml (19-117 ml) after PAE. There were no major complications and, in most cases (14/18) a volume reduction of the benign prostate hyperplasia occurred. CONCLUSION Performed by an experienced examiner CEUS with parametric imaging and TIC-analysis is highly useful to further establish prostatic artery embolization (PAE) as a successful minimal invasive treatment of benign prostatic hyperplasia.
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Affiliation(s)
- E M Jung
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - T Wertheimer
- Department for Internal Medicine III, Hematology and Oncology, University Medical Center Regensburg, Regensburg, Germany
| | - F J Putz
- Department of Nephrology, University Medical Center Regensburg, Regensburg, Germany
| | - F Jung
- Brandenburgische Technische Universität Cottbus-Senftenberg, Institute of Biotechnology, Cottbus, Germany
| | - S Kammerer
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - B Pregler
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - L Luerken
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - C Stroszczynski
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - L Beyer
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
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Shah M, Naik N, Somani BK, Hameed BMZ. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study. Turk J Urol 2020; 46:S27-S39. [PMID: 32479253 PMCID: PMC7731952 DOI: 10.5152/tud.2020.20117] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 04/12/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. MATERIAL AND METHODS Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. RESULTS The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively. CONCLUSIONS In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.
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Affiliation(s)
- Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
| | - Nithesh Naik
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K. Somani
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Urological Surgery, University Hospital Southampton NHS Trust, Southampton, UK
| | - BM Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- KMC Innovation Centre, Manipal Academy of Higher Education, Manipal, Karnataka, India
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50
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Lughezzani G, Maffei D, Saita A, Paciotti M, Diana P, Buffi NM, Colombo P, Elefante GM, Hurle R, Lazzeri M, Guazzoni G, Casale P. Diagnostic Accuracy of Microultrasound in Patients with a Suspicion of Prostate Cancer at Magnetic Resonance Imaging: A Single-institutional Prospective Study. Eur Urol Focus 2020; 7:1019-1026. [PMID: 33069624 DOI: 10.1016/j.euf.2020.09.013] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/26/2020] [Accepted: 09/22/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (MRI) represents the gold standard for the diagnosis of clinically significant prostate cancer (csPCa). The search for alternative diagnostic techniques is still ongoing. OBJECTIVE To determine the accuracy of microultrasound (microUS) for the diagnosis of csPCa within prospectively collected cohort of patients with a suspicion of prostate cancer (PCa) according to MRI. DESIGN, SETTING, AND PARTICIPANTS A total of 320 consecutive patients with at least one Prostate Imaging Reporting and Data System (PIRADS) ≥3 lesion according to MRI were prospectively enrolled. INTERVENTION All patients received microUS before prostate biopsy using the ExactVu system; the Prostate Risk Identification using microUS (PRI-MUS) protocol was used to identify targets. The urologists were blinded to MRI results until after the microUS targeting was completed. All patients received both targeted (based on either microUS or MRI findings) and randomized biopsies. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The sensitivity and specificity of microUS to determine the presence of csPCa (defined as at least one core with a Gleason score ≥7 PCa) were determined. Multivariable logistic regression analysis was fitted to determine the predictors of csPCa. RESULTS AND LIMITATIONS Clinically significant PCa was diagnosed in 116 (36.3%) patients. The sensitivity and negative predictive value of microUS for csPCa diagnosis were 89.7% and 81.5%, while specificity and positive predictive value were 26.0% and 40.8%, respectively. A combination of microUS-targeted and randomized biopsies would allow diagnosing the same proportion of csPCa as that diagnosed by an approach combining MRI-targeted and randomized biopsies (n = 113; 97.4%), with only three (2.6%) csPCa cases diagnosed by a microUS-targeted and three (2.6%) by an MRI-targeted approach. In a logistic regression model, an increasing PRI-MUS score was an independent predictor of csPCa (p ≤ 0.005). The main limitation of the current study is represented by the fact that all patients had suspicious MRI. CONCLUSIONS Microultrasound is a promising imaging modality for targeted prostate biopsies. Our results suggest that a microUS-based biopsy strategy may be capable of diagnosing the great majority of cancers, while missing only few patients with csPCa. PATIENT SUMMARY According to our results, microultrasound (microUS) may represent an effective diagnostic alternative to magnetic resonance imaging for the diagnosis of clinically significant prostate cancer, providing high sensitivity and a high negative predictive value. Further randomized studies are needed to confirm the potential role of microUS in the diagnostic pathway of patients with a suspicion of prostate cancer.
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Affiliation(s)
- Giovanni Lughezzani
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy; Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy.
| | - Davide Maffei
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy; Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | - Alberto Saita
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy
| | - Marco Paciotti
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy; Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | - Pietro Diana
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy; Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | - Nicolò Maria Buffi
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy; Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | | | | | - Rodolfo Hurle
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy
| | - Massimo Lazzeri
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy
| | - Giorgio Guazzoni
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy; Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | - Paolo Casale
- Department of Urology, Humanitas Clinical and Research Center, Rozzano, Italy
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