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Wang L, Cong R, Chen Z, Li D, Feng B, Liang M, Wang S, Ma X, Zhao X. Determination of prognostic predictors in patients with solitary hepatocellular carcinoma: histogram analysis of multiparametric MRI. Abdom Radiol (NY) 2023; 48:3362-3372. [PMID: 37561148 DOI: 10.1007/s00261-023-04015-8] [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: 05/27/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE To evaluate the histogram parameters of preoperative multiparametric magnetic resonance imaging (MRI) and clinical-radiological (CR) characteristics as prognostic predictors in patients with solitary hepatocellular carcinoma ≤ 5 cm and to determine the optimal time window for histogram analysis. METHODS We retrospectively included 151 patients who underwent preoperative MRI between January 2012 and December 2017. All patients were randomly separated into training and validation cohorts (n = 105 and 46). Eight whole-lesion histogram parameters were extracted from T2-weighted images, apparent diffusion coefficient maps, and dynamic contrast-enhanced images. Univariate and multivariate logistic regression analyses were performed to evaluate these histogram parameters and CR variables related to early recurrence (ER) and recurrence-free survival. A nomogram was derived from the clinical-radiological-histogram (CRH) model that incorporated these risk factors. Kaplan-Meier survival analysis was performed to evaluate the prognostic performance of the CRH model. RESULTS In total, 151 patients (male: female, 130: 21; median age, 54.46 ± 9.09 years) were evaluated. Multivariate logistic regression analysis revealed that the significant risk factors of ER were Mean Absolute Deviation and Minimum in the histogram analysis of the delayed phase images, as well as three important CR variables: albumin-bilirubin grade, microvascular invasion, and tumor size. The nomogram built by incorporating these risk factors showed satisfactory predictive ability in the training and validation cohorts with AUC values of 0.747 and 0.765, respectively. Furthermore, the prognostic nomogram can effectively classify patients into high- and low-risk groups (p < 0.05). CONCLUSION Multiparametric MRI-derived histogram parameters provide additional value in predicting patient prognosis. The CRH model may be a useful and noninvasive method for achieving prognostic stratification and personalized disease management.
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Affiliation(s)
- Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zhaowei Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, 100176, China
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Gibala S, Obuchowicz R, Lasek J, Schneider Z, Piorkowski A, Pociask E, Nurzynska K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. J Clin Med 2023; 12:jcm12082836. [PMID: 37109173 PMCID: PMC10146387 DOI: 10.3390/jcm12082836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. METHODS Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. RESULTS Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. CONCLUSIONS There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.
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Affiliation(s)
- Sebastian Gibala
- Urology Department, Ultragen Medical Center, 31-572 Krakow, Poland
| | - Rafal Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
| | - Julia Lasek
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Zofia Schneider
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Adam Piorkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Elżbieta Pociask
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Karolina Nurzynska
- Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland
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Souza SAS, Reis LO, Alves AFF, Silva LC, Medeiros MCK, Andrade DL, Billis A, Amaro JL, Martins DL, Trindade AP, Miranda JRA, Pina DR. Multiple analyses suggests texture features can indicate the presence of tumor in the prostate tissue. Phys Eng Sci Med 2022; 45:525-535. [PMID: 35325377 DOI: 10.1007/s13246-022-01118-2] [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: 06/03/2021] [Accepted: 03/09/2022] [Indexed: 10/18/2022]
Abstract
Several studies have demonstrated statistical and texture analysis abilities to differentiate cancerous from healthy tissue in magnetic resonance imaging. This study developed a method based on texture analysis and machine learning to differentiate prostate findings. Forty-eight male patients with PI-RADS classification and subsequent radical prostatectomy histopathological analysis were used as gold standard. Experienced radiologists delimited the regions of interest in magnetic resonance images. Six different groups of images were used to perform multiple analyses (seven analyses variations). Those analyses were outlined by specialists in urology as those of most significant importance for the classification. Forty texture features were extracted from each image and processed with Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Those seven analyses variation results were described in terms of area under the ROC curve (AUC), accuracy, F-score, precision and sensitivity. The highest AUC (93.7%) and accuracy (88.8%) were obtained when differentiating the group with both MRI and histopathology positive findings against the group with both negative MRI and histopathology. When differentiating the group with both MRI and histopathology positive findings versus the peripheral image zone group the AUC value was 86.6%. When differentiating the group with negative MRI/positive histopathology versus the group with both negative MRI and histopathology the AUC value was 80.7%. The evaluation of statistical and texture analysis promoted very suggestive indications for future work in prostate cancer suspicious regions. The method is fast for both region of interest selection and classification with machine learning and the result brings original contributions in the classification of different groups of patients. This tool is low-cost, and can be used to assist diagnostic decisions.
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Affiliation(s)
- Sérgio Augusto Santana Souza
- São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 18618-689, Brazil
| | - Leonardo Oliveira Reis
- Department of Urology, UroScience, State University of Campinas, Unicamp and Pontifical Catholic University of Campinas, PUC-Campinas, Av. John Boyd Dunlop-Jardim Ipaussurama, Campinas, SP, CEP: 13034-685, Brazil
| | - Allan Felipe Fattori Alves
- Botucatu Medical School, Clinics Hospital, Medical Physics and Radioprotection Nucleus, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP: 18618687, Brazil
| | - Letícia Cotinguiba Silva
- São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 18618-689, Brazil
| | | | - Danilo Leite Andrade
- Department of Urology, UroScience, State University of Campinas, Unicamp and Pontifical Catholic University of Campinas, PUC-Campinas, Av. John Boyd Dunlop-Jardim Ipaussurama, Campinas, SP, CEP: 13034-685, Brazil
| | - Athanase Billis
- Department of Anatomic Pathology and Urology, School of Medical Sciences, State University of Campinas (Unicamp), Campinas, Brazil
| | - João Luiz Amaro
- Department of Urology, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | | | - André Petean Trindade
- Botucatu Medical School, São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP:18618687, Brazil
| | - José Ricardo Arruda Miranda
- Institute of Bioscience, São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 8618-689, Brazil
| | - Diana Rodrigues Pina
- Botucatu Medical School, São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP:18618687, Brazil.
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Tsuruta C, Hirata K, Kudo K, Masumori N, Hatakenaka M. DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones. Eur Radiol Exp 2022; 6:1. [PMID: 35018507 PMCID: PMC8752657 DOI: 10.1186/s41747-021-00252-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/25/2021] [Indexed: 11/10/2022] Open
Abstract
Background We investigated the correlation between texture features extracted from apparent diffusion coefficient (ADC) maps or diffusion-weighted images (DWIs), and grade group (GG) in the prostate peripheral zone (PZ) and transition zone (TZ), and assessed reliability in repeated examinations. Methods Patients underwent 3-T pelvic magnetic resonance imaging (MRI) before radical prostatectomy with repeated DWI using b-values of 0, 100, 1,000, and 1,500 s/mm2. Region of interest (ROI) for cancer was assigned to the first and second DWI acquisition separately. Texture features of ROIs were extracted from comma-separated values (CSV) data of ADC maps generated from several sets of two b-value combinations and DWIs, and correlation with GG, discrimination ability between GG of 1–2 versus 3–5, and data repeatability were evaluated in PZ and TZ. Results Forty-four patients with 49 prostate cancers met the eligibility criteria. In PZ, ADC 10% and 25% based on ADC map of two b-value combinations of 100 and 1,500 s/mm2 and 10% based on ADC map with b-value of 0 and 1,500 s/mm2 showed significant correlation with GG, acceptable discrimination ability, and good repeatability. In TZ, higher-order texture feature of busyness extracted from ADC map of 100 and 1,500 s/mm2, and high gray-level run emphasis, short-run high gray-level emphasis, and high gray-level zone emphasis from DWI with b-value of 100 s/mm2 demonstrated significant correlation, excellent discrimination ability, but moderate repeatability. Conclusions Some DWI-related features showed significant correlation with GG, acceptable to excellent discrimination ability, and moderate to good data repeatability in prostate cancer, and differed between PZ and TZ. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-021-00252-y.
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Affiliation(s)
- Chie Tsuruta
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.,Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Naoya Masumori
- Department of Urology, School of Medicine, Sapporo Medical University, Sapporo, Japan
| | - Masamitsu Hatakenaka
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan.
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Pecoraro M, Messina E, Bicchetti M, Carnicelli G, Del Monte M, Iorio B, La Torre G, Catalano C, Panebianco V. The future direction of imaging in prostate cancer: MRI with or without contrast injection. Andrology 2021; 9:1429-1443. [PMID: 33998173 DOI: 10.1111/andr.13041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/23/2021] [Accepted: 05/05/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Multiparametric MRI (mpMRI) is the "state of the art" management tool for patients with suspicion of prostate cancer (PCa). The role of non-contrast MRI is investigated to move toward a more personalized, less invasive, and highly cost-effective PCa diagnostic workup. OBJECTIVE To perform a non-systematic review of the existing literature to highlight strength and flaws of performing non-contrast MRI, and to provide a critical overview of the international scientific production on the topic. MATERIALS AND METHODS Online databases (Medline, PubMed, and Web of Science) were searched for original articles, systematic review and meta-analysis, and expert opinion papers. RESULTS Several investigations have shown comparable diagnostic accuracy of biparametric (bpMRI) and mpMRI for the detection of PCa. The advantage of abandoning contrast-enhanced sequences improves operational logistics, lowering costs, acquisition time, and side effects. The main limitations of bpMRI are that most studies comparing non-contrast with contrast MRI come from centers with high expertise that might not be reproducible in the general community setting; besides, reduced protocols might be insufficient for estimation of the intra- and extra-prostatic extension and regional disease. The mentioned observations suggest that low-quality mpMRI for the general population might represent the main shortage to overcome. DISCUSSION Non-contrast MRI future trends are likely represented by PCa screening and the application of artificial intelligence (AI) tools. PCa screening is still a controversial topic; bpMRI has become one of the most promising diagnostic applications, as it is a more sensitive test for PCa early detection, compared to serum PSA level test. Also, AI applications and radiomic have been the object of several studies investigating PCa detection using bpMRI, showing encouraging results. CONCLUSION Today, the accessibility to MRI for early detection of PCa is a priority. Results from prospective, multicenter, multireader, and paired validation studies are needed to provide evidence supporting its role in the clinical practice.
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Affiliation(s)
- Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Emanuele Messina
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Marco Bicchetti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Giorgia Carnicelli
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Maurizio Del Monte
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Beniamino Iorio
- Department of Surgical Sciences, "Tor Vergata" University of Rome, Rome, Italy
| | - Giuseppe La Torre
- Department of Public Health and Infectious Disease, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
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