1
|
Kedves A, Akay M, Akay Y, Kisiván K, Glavák C, Miovecz Á, Schiffer Á, Kisander Z, Lőrincz A, Szőke A, Sánta B, Freihat O, Sipos D, Kovács Á, Lakosi F. Predictive value of magnetic resonance imaging diffusion parameters using artificial intelligence in low-and intermediate-risk prostate cancer patients treated with stereotactic ablative radiotherapy: A pilot study. Radiography (Lond) 2024; 30:986-994. [PMID: 38678978 DOI: 10.1016/j.radi.2024.03.015] [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: 09/12/2023] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 05/01/2024]
Abstract
INTRODUCTION To investigate the predictive value of the pre-treatment diffusion parameters of diffusion-weighted magnetic resonance imaging (DW-MRI) using artificial intelligence (AI) for prostate-specific antigen (PSA) response in patients with low- and intermediate-risk prostate cancer (PCa) treated with stereotactic ablative radiotherapy (SABR). METHODS Retrospective evaluation was performed for 30 patients using pre-treatment multi-parametric MR image datasets between 2017 and 2021. MR-based mean- and minimum apparent diffusion coefficients (ADCmean, ADCmin) were calculated for the intraprostatic dominant lesion. Therapeutic response was assessed using PSA levels. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis. Statistics performed with a significance level of p ≤ 0.05. RESULTS No biochemical relapse was detected after a median follow-up of twenty-three months (range: 3-50), with a median PSA of 0.01 ng/ml (range: 0.006-2.8) at the last examination. Significant differences were observed between the pre-treatment ADCmean, ADCmin parameters, and the group averages of patients with low and high 1-year-PSA measurements (p < 0.0001, p < 0.0001). In prediction, the random forest (RF) model outperformed the decision tree (DT) and support vector machine (SVM) models by yielding area under the curves (AUC), with 0.722, 0.685, and 0.5, respectively. CONCLUSION Our findings suggest that pre-treatment MR diffusion data may predict therapeutic response using the novel approach of machine learning in PCa patients treated with SABR. IMPLICATIONS FOR PRACTICE Clinicians shall measure and implement the evaluation of the suggested parameters (ADCmin, ADCmean) to provide the most accurate therapy for the patient.
Collapse
Affiliation(s)
- A Kedves
- "Moritz Kaposi" Teaching Hospital, Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, Kaposvár, Hungary; Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Pécs, Hungary; Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary
| | - M Akay
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Y Akay
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - K Kisiván
- "Moritz Kaposi" Teaching Hospital, Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, Kaposvár, Hungary
| | - C Glavák
- "Moritz Kaposi" Teaching Hospital, Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, Kaposvár, Hungary
| | - Á Miovecz
- "Moritz Kaposi" Teaching Hospital, Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, Kaposvár, Hungary; Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary
| | - Á Schiffer
- Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Pécs, Hungary
| | - Z Kisander
- Department of Electrical Networks, Faculty of Engineering and Information Technology, University of Pécs, Pécs, Hungary
| | - A Lőrincz
- Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Pécs, Hungary; Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - A Szőke
- 3D Printing and Visualization Centre, Medical School, University of Pécs, Pécs, Hungary
| | - B Sánta
- Röntgenpraxis Dr. Thomas Trieb, Innsbruck, Austria
| | - O Freihat
- College of Health Sciences, Abu Dhabi University, Abu Dhabi, UAE
| | - D Sipos
- "Moritz Kaposi" Teaching Hospital, Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, Kaposvár, Hungary; Institute of Diagnostics, Faculty of Health Sciences, University of Pécs, Pécs, Hungary
| | - Á Kovács
- Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary; Institute of Diagnostics, Faculty of Health Sciences, University of Pécs, Pécs, Hungary; Department of Oncoradiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - F Lakosi
- "Moritz Kaposi" Teaching Hospital, Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, Kaposvár, Hungary; Doctoral School of Health Sciences, University of Pécs, Pécs, Hungary; Institute of Diagnostics, Faculty of Health Sciences, University of Pécs, Pécs, Hungary.
| |
Collapse
|
2
|
Ozbozduman K, Loc I, Durmaz S, Atasoy D, Kilic M, Yildirim H, Esen T, Vural M, Unlu MB. Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology. Sci Rep 2024; 14:5849. [PMID: 38462645 PMCID: PMC10925603 DOI: 10.1038/s41598-024-56415-5] [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: 12/30/2023] [Accepted: 03/06/2024] [Indexed: 03/12/2024] Open
Abstract
This study aimed to enhance the accuracy of Gleason grade group (GG) upgrade prediction in prostate cancer (PCa) patients who underwent MRI-guided in-bore biopsy (MRGB) and radical prostatectomy (RP) through a combined analysis of prebiopsy and MRGB clinical data. A retrospective analysis of 95 patients with prostate cancer diagnosed by MRGB was conducted where all patients had undergone RP. Among the patients, 64.2% had consistent GG results between in-bore biopsies and RP, whereas 28.4% had upgraded and 7.4% had downgraded results. GG1 biopsy results, lower biopsy core count, and fewer positive cores were correlated with upgrades in the entire patient group. In patients with GG > 1 , larger tumor sizes and fewer biopsy cores were associated with upgrades. By integrating MRGB data with prebiopsy clinical data, machine learning (ML) models achieved 85.6% accuracy in predicting upgrades, surpassing the 64.2% baseline from MRGB alone. ML analysis also highlighted the value of the minimum apparent diffusion coefficient ( ADC min ) for GG > 1 patients. Incorporation of MRGB results with tumor size, ADC min value, number of biopsy cores, positive core count, and Gleason grade can be useful to predict GG upgrade at final pathology and guide patient selection for active surveillance.
Collapse
Affiliation(s)
| | - Irem Loc
- Bogazici University Physics Department, Istanbul, Turkey
| | - Selahattin Durmaz
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Duygu Atasoy
- Department of Radiology, University of Koc School of Medicine, Istanbul, Turkey
| | - Mert Kilic
- Department of Urology, VKF American Hospital, Istanbul, Turkey
| | - Hakan Yildirim
- Department of Radiology, VKF American Hospital, Istanbul, Turkey
| | - Tarik Esen
- Department of Urology, VKF American Hospital, Istanbul, Turkey
- Department of Urology, University of Koc School of Medicine, Istanbul, Turkey
| | - Metin Vural
- Department of Radiology, VKF American Hospital, Istanbul, Turkey
| | - M Burcin Unlu
- Faculty of Engineering, Ozyegin University, Istanbul, Turkey
- Faculty of Aviation and Aeronautical Sciences Ozyegin University, Istanbul, Turkey
| |
Collapse
|
3
|
Zhang M, Lyu S, Yang L, Wei H, Liu R, Wang X, Liu Y, Zhang B, Kwok JKS, Zhang Y. A nomogram based on ultrasound radiomics for predicting the invasiveness of cN0 single papillary thyroid microcarcinoma. Gland Surg 2023; 12:1735-1745. [PMID: 38229850 PMCID: PMC10788574 DOI: 10.21037/gs-23-473] [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: 11/18/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024]
Abstract
Background Up to 15.3% of papillary thyroid microcarcinoma (PTMC) patients with negative clinical lymph node metastasis (cN0) were confirmed to have pathological lymph node metastasis in level VI. Conventional ultrasound (US) focuses on the characteristics of tumor capsule and the periphery to determine whether the tumor has invasive growth. However, due to its small size, the typical features of invasiveness shown by conventional 2-dimensional (2D) US are not well visualized. US-based radiomics makes use of artificial intelligence and big data to build a model that can help improving diagnostic accuracy and providing prognostic implication of the disease. We hope to establish and assess the value of a nomogram based on US radiomics combined with independent risk factors in predicting the invasiveness of a single PTMC without clinical lymph node metastasis (cN0). Methods A total of 317 patients with cN0 single PTMC who underwent US examination and operation were included in this retrospective cohort study. Patients were randomly divided into training and testing set in the ratio of 8:2. The US images of all patients were segmented, and the radiomics features were extracted. In the training dataset, the US with features of minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were selected and radiomics signatures were then established according to their respective weighting coefficients. Univariate and multivariate logistic regression analyses were employed to generate the risk factors of possible invasive PTMC. The nomogram is then made by combining high risk factors and the radiomics signature. The efficiency of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and its clinical application value was assessed by decision curve analysis (DCA). The testing dataset was used to validate the model. Results In the model, seven radiomics features were selected to establish the radiomics signature. A nomogram was made by incorporating clinically independent risk factors and the radiomics signature. Both the ROC curve and calibration curve showed good prediction efficiency. The area under the curve (AUC), accuracy, sensitivity, and specificity of the nomogram in the training data were 0.76 [95% confidence interval (CI): 0.71-0.82], 0.811, 0.914, and 0.727, respectively whereas the results of the testing dataset were 0.71 (95% CI: 0.58-0.84), 0.841, 0.533, and 0.868. As such, the efficacy of the nomogram in predicting the invasiveness of PTMC was subsequently validated by the DCA. Conclusions Nomogram based on thyroid US radiomics has an excellent predictive value of the potential invasiveness of a single PTMC without clinical lymph node metastasis. With these promising results, it can potentially be the imaging marker used in daily clinical practice.
Collapse
Affiliation(s)
- Meiwu Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Shuyi Lyu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Liu Yang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Huilin Wei
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Rui Liu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Xin Wang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Yi Liu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Baisong Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | | | - Yan Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| |
Collapse
|
4
|
Valeri A, Nguyen TA. Research on texture images and radiomics in urology: a review of urological MR imaging applications. Curr Opin Urol 2023; 33:428-436. [PMID: 37727910 DOI: 10.1097/mou.0000000000001131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
PURPOSE OF REVIEW Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives. RECENT FINDINGS Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization. SUMMARY Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.
Collapse
Affiliation(s)
- Antoine Valeri
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
- CeRePP, Paris, France
| | - Truong An Nguyen
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
| |
Collapse
|
5
|
Qiao X, Gu X, Liu Y, Shu X, Ai G, Qian S, Liu L, He X, Zhang J. MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer. Cancers (Basel) 2023; 15:4536. [PMID: 37760505 PMCID: PMC10526397 DOI: 10.3390/cancers15184536] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/02/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. METHODS A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. RESULTS The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). CONCLUSION The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method.
Collapse
Affiliation(s)
- Xiaofeng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (X.Q.); (X.G.); (Y.L.); (X.S.); (G.A.)
| | - Xiling Gu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (X.Q.); (X.G.); (Y.L.); (X.S.); (G.A.)
| | - Yunfan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (X.Q.); (X.G.); (Y.L.); (X.S.); (G.A.)
| | - Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (X.Q.); (X.G.); (Y.L.); (X.S.); (G.A.)
| | - Guangyong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (X.Q.); (X.G.); (Y.L.); (X.S.); (G.A.)
| | - Shuang Qian
- Big Data and Software Engineering College, Chongqing University, Chongqing 400000, China; (S.Q.); (L.L.)
| | - Li Liu
- Big Data and Software Engineering College, Chongqing University, Chongqing 400000, China; (S.Q.); (L.L.)
| | - Xiaojing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (X.Q.); (X.G.); (Y.L.); (X.S.); (G.A.)
| | - Jingjing Zhang
- Departments of Diagnostic Radiology, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, National University of Singapore, Singapore 117599, Singapore
| |
Collapse
|
6
|
Midya A, Hiremath A, Huber J, Sankar Viswanathan V, Omil-Lima D, Mahran A, Bittencourt LK, Harsha Tirumani S, Ponsky L, Shiradkar R, Madabhushi A. Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: preliminary findings. Front Oncol 2023; 13:1166047. [PMID: 37731630 PMCID: PMC10508842 DOI: 10.3389/fonc.2023.1166047] [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: 02/14/2023] [Accepted: 07/24/2023] [Indexed: 09/22/2023] Open
Abstract
Objective The aim of this study was to quantify radiomic changes in prostate cancer (PCa) progression on serial MRI among patients on active surveillance (AS) and evaluate their association with pathologic progression on biopsy. Methods This retrospective study comprised N = 121 biopsy-proven PCa patients on AS at a single institution, of whom N = 50 at baseline conformed to the inclusion criteria. ISUP Gleason Grade Groups (GGG) were obtained from 12-core TRUS-guided systematic biopsies at baseline and follow-up. A biopsy upgrade (AS+) was defined as an increase in GGG (or in number of positive cores) and no upgrade (AS-) was defined when GGG remained the same during a median period of 18 months. Of N = 50 patients at baseline, N = 30 had MRI scans available at follow-up (median interval = 18 months) and were included for delta radiomic analysis. A total of 252 radiomic features were extracted from the PCa region of interest identified by board-certified radiologists on 3T bi-parametric MRI [T2-weighted (T2W) and apparent diffusion coefficient (ADC)]. Delta radiomic features were computed as the difference of radiomic feature between baseline and follow-up scans. The association of AS+ with age, prostate-specific antigen (PSA), Prostate Imaging Reporting and Data System (PIRADS v2.1) score, and tumor size was evaluated at baseline and follow-up. Various prediction models were built using random forest (RF) classifier within a threefold cross-validation framework leveraging baseline radiomics (Cbr), baseline radiomics + baseline clinical (Cbrbcl), delta radiomics (CΔr), delta radiomics + baseline clinical (CΔrbcl), and delta radiomics + delta clinical (CΔrΔcl). Results An AUC of 0.64 ± 0.09 was obtained for Cbr, which increased to 0.70 ± 0.18 with the integration of clinical variables (Cbrbcl). CΔr yielded an AUC of 0.74 ± 0.15. Integrating delta radiomics with baseline clinical variables yielded an AUC of 0.77 ± 0.23. CΔrΔclresulted in the best AUC of 0.84 ± 0.20 (p < 0.05) among all combinations. Conclusion Our preliminary findings suggest that delta radiomics were more strongly associated with upgrade events compared to PIRADS and other clinical variables. Delta radiomics on serial MRI in combination with changes in clinical variables (PSA and tumor volume) between baseline and follow-up showed the strongest association with biopsy upgrade in PCa patients on AS. Further independent multi-site validation of these preliminary findings is warranted.
Collapse
Affiliation(s)
- Abhishek Midya
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States
| | | | - Jacob Huber
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | | | | | - Amr Mahran
- Department of Urology, Assiut University, Asyut, Egypt
| | - Leonardo K. Bittencourt
- Department of Radiology, University Hospitals, Cleveland Medical Center, Cleveland, OH, United States
| | - Sree Harsha Tirumani
- Department of Radiology, University Hospitals, Cleveland Medical Center, Cleveland, OH, United States
| | - Lee Ponsky
- Department of Urology, University Hospitals, Cleveland Medical Center, Cleveland, OH, United States
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, United States
- Atlanta Veterans Administration Medical Center, Atlanta, GA, United States
| |
Collapse
|
7
|
Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, Liu JG. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments? Mil Med Res 2023; 10:29. [PMID: 37357263 DOI: 10.1186/s40779-023-00464-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023] Open
Abstract
The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.
Collapse
Affiliation(s)
- Li-Tao Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhen-Yu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Wan-Fang Xie
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Li-Zhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Peking University, 100191, Beijing, China.
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
| | - Jian-Gang Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100029, China.
| |
Collapse
|
8
|
Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
Collapse
Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| |
Collapse
|
9
|
Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, Turkbey B. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. J Am Coll Radiol 2023; 20:134-145. [PMID: 35922018 PMCID: PMC9887098 DOI: 10.1016/j.jacr.2022.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research. MATERIALS AND METHODS This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons. RESULTS In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation. CONCLUSION A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.
Collapse
Affiliation(s)
- Mason J Belue
- Medical Research Scholars Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan S Lay
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Intramural Research Training Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Tim E Phelps
- Postdoctoral Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Artificial Intelligence Resource, Chief of Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Senior Clinician/Director, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
| |
Collapse
|
10
|
Zhuang J, Kan Y, Wang Y, Marquis A, Qiu X, Oderda M, Huang H, Gatti M, Zhang F, Gontero P, Xu L, Calleris G, Fu Y, Zhang B, Marra G, Guo H. Machine Learning-Based Prediction of Pathological Upgrade From Combined Transperineal Systematic and MRI-Targeted Prostate Biopsy to Final Pathology: A Multicenter Retrospective Study. Front Oncol 2022; 12:785684. [PMID: 35463339 PMCID: PMC9021959 DOI: 10.3389/fonc.2022.785684] [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: 09/29/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
Objective This study aimed to evaluate the pathological concordance from combined systematic and MRI-targeted prostate biopsy to final pathology and to verify the effectiveness of a machine learning-based model with targeted biopsy (TB) features in predicting pathological upgrade. Materials and Methods All patients in this study underwent prostate multiparametric MRI (mpMRI), transperineal systematic plus transperineal targeted prostate biopsy under local anesthesia, and robot-assisted laparoscopic radical prostatectomy (RARP) for prostate cancer (PCa) sequentially from October 2016 to February 2020 in two referral centers. For cores with cancer, grade group (GG) and Gleason score were determined by using the 2014 International Society of Urological Pathology (ISUP) guidelines. Four supervised machine learning methods were employed, including two base classifiers and two ensemble learning-based classifiers. In all classifiers, the training set was 395 of 565 (70%) patients, and the test set was the remaining 170 patients. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The Gini index was used to evaluate the importance of all features and to figure out the most contributed features. A nomogram was established to visually predict the risk of upgrading. Predicted probability was a prevalence rate calculated by a proposed nomogram. Results A total of 515 patients were included in our cohort. The combined biopsy had a better concordance of postoperative histopathology than a systematic biopsy (SB) only (48.15% vs. 40.19%, p = 0.012). The combined biopsy could significantly reduce the upgrading rate of postoperative pathology, in comparison to SB only (23.30% vs. 39.61%, p < 0.0001) or TB only (23.30% vs. 40.19%, p < 0.0001). The most common pathological upgrade occurred in ISUP GG1 and GG2, accounting for 53.28% and 20.42%, respectively. All machine learning methods had satisfactory predictive efficacy. The overall accuracy was 0.703, 0.768, 0.794, and 0.761 for logistic regression, random forest, eXtreme Gradient Boosting, and support vector machine, respectively. TB-related features were among the most contributed features of a prediction model for upgrade prediction. Conclusion The combined effect of SB plus TB led to a better pathological concordance rate and less upgrading from biopsy to RP. Machine learning models with features of TB to predict PCa GG upgrading have a satisfactory predictive efficacy.
Collapse
Affiliation(s)
- Junlong Zhuang
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Urology, Nanjing University, Nanjing, China
| | - Yansheng Kan
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yuwen Wang
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Urology, Nanjing University, Nanjing, China.,Medical School of Southeast University, Nanjing Drum Tower Hospital, Nanjing, China
| | - Alessandro Marquis
- Department of Urology, San Giovanni Battista Hospital, Città della Salute e della Scienza and University of Turin, Turin, Italy
| | - Xuefeng Qiu
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Urology, Nanjing University, Nanjing, China
| | - Marco Oderda
- Department of Urology, San Giovanni Battista Hospital, Città della Salute e della Scienza and University of Turin, Turin, Italy
| | - Haifeng Huang
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Urology, Nanjing University, Nanjing, China
| | - Marco Gatti
- Department of Radiology, San Giovanni Battista Hospital, Città della Salute e della Scienza and University of Turin, Turin, Italy
| | - Fan Zhang
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Urology, Nanjing University, Nanjing, China
| | - Paolo Gontero
- Department of Urology, San Giovanni Battista Hospital, Città della Salute e della Scienza and University of Turin, Turin, Italy
| | - Linfeng Xu
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Urology, Nanjing University, Nanjing, China
| | - Giorgio Calleris
- Department of Urology, San Giovanni Battista Hospital, Città della Salute e della Scienza and University of Turin, Turin, Italy
| | - Yao Fu
- Department of Pathology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Giancarlo Marra
- Department of Urology, San Giovanni Battista Hospital, Città della Salute e della Scienza and University of Turin, Turin, Italy.,Department of Urology and Clinical Research Group on Predictive Onco-Urology, APHP, Sorbonne University, Paris, France
| | - Hongqian Guo
- Department of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Urology, Nanjing University, Nanjing, China
| |
Collapse
|
11
|
Wang L, Margolis DJ, Chen M, Zhao X, Li Q, Yang Z, Tian J, Wang Z. Quality in MR reporting of the prostate – improving acquisition, the role of AI and future perspectives. Br J Radiol 2022; 95:20210816. [PMID: 35119914 PMCID: PMC8978223 DOI: 10.1259/bjr.20210816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The high quality of MRI reporting of the prostate is the most critical component of the service provided by a radiologist. Prostate MRI structured reporting with PI-RADS v. 2.1 has been proven to improve consistency, quality, guideline-based care in the management of prostate cancer. There is room for improved accuracy of prostate mpMRI reporting, particularly as PI-RADS core criteria are subjective for radiologists. The application of artificial intelligence may support radiologists in interpreting MRI scans. This review addresses the quality of prostate multiparametric MRI (mpMRI) structured reporting (include improvements in acquisition using artificial intelligence) in terms of size of prostate gland, imaging quality, lesion location, lesion size, TNM staging, sector map, and discusses the future prospects of quality in MR reporting.
Collapse
Affiliation(s)
- Liang Wang
- Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China
| | - Daniel J. Margolis
- Department of Radiology, Weill Cornell Medicine/ New York Presbyterian, New York, United States
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 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, China
| | - Qiubai Li
- Department of Radiology, University of Iowa, Roy Carver College of Medicine, Iowa, United States
| | - Zhenghan Yang
- Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China
| | | | - Zhenchang Wang
- Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China
| |
Collapse
|
12
|
Chen J, Lu S, Mao Y, Tan L, Li G, Gao Y, Tan P, Huang D, Zhang X, Qiu Y, Liu Y. An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: a multi-cohort study. Eur Radiol 2022; 32:1548-1557. [PMID: 34665315 DOI: 10.1007/s00330-021-08292-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/01/2021] [Accepted: 07/12/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To explore whether radiomics features extracted from pre-treatment magnetic resonance imaging (MRI) can predict the overall survival (OS) in patients with hypopharyngeal squamous cell carcinoma. METHODS A total of 190 patients with hypopharyngeal squamous cell carcinoma were eligibly enrolled from two institutions. Radiomics features were extracted from contrast-enhanced axial T1-weighted (CE-T1WI) sequence. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomics score correlated with OS. Multivariate logistic regression analysis was applied to determine the independent risk factors, which was combined with radiomics score to build the final radiomics nomogram. RESULTS A radiomics score with 6 CE-T1WI features for OS prediction was constructed and validated; its integration with specific clinicopathologic factors (N stage) showed a better prediction performance in the training, internal validation, and external validation cohorts (C-index 0.78, 0.75, and 0.75). Calibration curves determined a good agreement between the predicted and actual overall survival. CONCLUSIONS The radiomics-clinical nomogram and radiomics score might be non-invasive and reliable methods for the risk stratification in patients with hypopharyngeal squamous cell carcinoma. KEY POINTS • An MRI-based radiomics model was constructed to evaluate of OS in patients with hypopharyngeal squamous cell carcinoma. • A radiomics-clinical nomogram that combined radiomics features and clinical characteristics was established. • Multi-cohort study validated the predictive performance of the radiomics-clinical nomogram to stratify patients with high risk in clinical practice.
Collapse
Affiliation(s)
- Juan Chen
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China
| | - Shanhong Lu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China
| | - Yitao Mao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Lei Tan
- College of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, 410205, Hunan, China
| | - Guo Li
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China
| | - Yan Gao
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China
| | - Pingqing Tan
- Department of Head and Neck Surgery, The Affiliated Tumor Hospital of Xiangya Medical School, Hunan Cancer Hospital, Central South University, Changsha, 410013, Hunan, China
| | - Donghai Huang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Xiangya Road, Changsha, 410008, Hunan, China
| | - Xin Zhang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Xiangya Road, Changsha, 410008, Hunan, China
| | - Yuanzheng Qiu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China.
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China.
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Xiangya Road, Changsha, 410008, Hunan, China.
| | - Yong Liu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, 410008, Hunan, China.
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, 410008, Hunan, China.
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Xiangya Road, Changsha, 410008, Hunan, China.
| |
Collapse
|
13
|
Liakos N, Witt JH, Rachubinski P, Leyh-Bannurah SR. The Dilemma of Misclassification Rates in Senior Patients With Prostate Cancer, Who Were Treated With Robot-Assisted Radical Prostatectomy: Implications for Patient Counseling and Diagnostics. Front Surg 2022; 9:838477. [PMID: 35252339 PMCID: PMC8888518 DOI: 10.3389/fsurg.2022.838477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/17/2022] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES There is a recent paradigm shift to extend robot-assisted radical prostatectomy (RARP) to very senior prostate cancer (PCa) patients based on biological fitness, comorbidities, and clinical PCa assessment that approximates the true risk of progression. Thus, we aimed to assess misclassification rates between clinical vs. pathological PCa burden. MATERIALS AND METHODS We compared senior patients with PCa ≥75 y (n = 847), who were propensity score matched with younger patients <75 y (n = 3,388) in a 1:4 ratio. Matching was based on the number of biopsy cores, prostate volume, and preoperative Cancer of the Prostate Risk Assessment (CAPRA) risk groups score. Multivariable logistic regression models (LRMs) predicted surgical CAPRA (CAPRA-S) upgrade, which was defined as a higher risk of the CAPRA-S in the presence of lower-risk preoperative CAPRA score. LRM incorporated the same variables as propensity score matching. Moreover, patients were categorized as low-, intermediate-, and high-risk, preoperative and according to their CAPRA and CAPRA-S scores. RESULTS Surgical CAPRA risk strata significantly differed between the groups. Greater proportions of unfavorable intermediate risk (39 vs. 32%) or high risk (30 vs. 28%; p < 0.001) were observed. These proportions are driven by greater proportions of International Society of Urological Pathology (ISUP) Gleason Grade Group 4 or 5 (33 vs. 26%; p = 0.001) and pathological tumor stage (≥T3a 54 vs. 45%; p < 0.001). Increasing age was identified as an independent predictor of CAPRA-S-based upgrade (age odds ratio [OR] 1.028 95% CI 1.02-1.037; p < 0.001). CONCLUSION Approximately every second senior patient has a misclassification in (i.e., any up or downgrade) and each 4.5th senior patient specifically has an upgrade in his final pathology that directly translates to an unfavorable PCa prognosis. It is imperative to take such substantial misclassification rates into account for this sensitive PCa demographic of senior men. Future prospective studies are warranted to further optimize PCa workflow and diagnostics, such as to incorporate modern imaging, molecular profiling and implement these into biopsy strategies to identify true PCa burden.
Collapse
Affiliation(s)
- Nikolaos Liakos
- Prostate Center Northwest, Department of Urology, Pediatric Urology and Uro-Oncology, St. Antonius-Hospital, Gronau, Germany
| | | | | | | |
Collapse
|