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Santucci D, Ragone R, Vergantino E, Vaccarino F, Esperto F, Prata F, Scarpa RM, Papalia R, Beomonte Zobel B, Grasso FR, Faiella E. Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features. Cancers (Basel) 2024; 16:2731. [PMID: 39123458 PMCID: PMC11311324 DOI: 10.3390/cancers16152731] [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: 06/27/2024] [Revised: 07/24/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
PURPOSE We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.
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Affiliation(s)
- Domiziana Santucci
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Raffaele Ragone
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Elva Vergantino
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Federica Vaccarino
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Francesco Esperto
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Francesco Prata
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Roberto Mario Scarpa
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Rocco Papalia
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (F.E.); (F.P.); (R.M.S.); (R.P.)
| | - Bruno Beomonte Zobel
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Francesco Rosario Grasso
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
| | - Eliodoro Faiella
- Department of Diagnostic Imaging, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (R.R.); (E.V.); (F.V.); (B.B.Z.); (F.R.G.); (E.F.)
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Faiella E, Vaccarino F, Ragone R, D’Amone G, Cirimele V, Piccolo CL, Vertulli D, Grasso RF, Zobel BB, Santucci D. Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review. J Clin Med 2023; 12:7032. [PMID: 38002646 PMCID: PMC10672480 DOI: 10.3390/jcm12227032] [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: 09/12/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies available in the literature to examine their initial findings. (2) Methods: Two reviewers conducted independently a search of MEDLINE databases, identifying articles exploring AI's role in PCa LNI. Sixteen studies were selected, and their methodological quality was appraised using the Radiomics Quality Score. (3) Results: AI models in Magnetic Resonance Imaging (MRI)-based studies exhibited comparable LNI prediction accuracy to standard nomograms. Computed Tomography (CT)-based and Positron Emission Tomography (PET)-CT models demonstrated high diagnostic and prognostic results. (4) Conclusions: AI models showed promising results in LN metastasis prediction and detection in PCa patients. Limitations of the reviewed studies encompass retrospective design, non-standardization, manual segmentation, and limited studies and participants. Further research is crucial to enhance AI tools' effectiveness in this area.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Domiziana Santucci
- Radiology Department, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Roma, Italy; (E.F.); (F.V.); (R.R.); (G.D.); (V.C.); (C.L.P.); (D.V.); (R.F.G.); (B.B.Z.)
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Wang H, Xia Z, Xu Y, Sun J, Wu J. The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis. Prostate Cancer Prostatic Dis 2023; 26:602-613. [PMID: 37488275 DOI: 10.1038/s41391-023-00704-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND In clinical practice, there are currently a variety of nomograms for predicting lymph node metastasis (LNM) of prostate cancer. At the same time, some scholars have introduced machine learning (ML) into the prediction of LNM of prostate cancer. However, the predictive value of nomograms and ML remains controversial. Based on this situation, this systematic review and meta-analysis was performed to explore the predictive value of various nomograms currently recommended and newly-developed ML models for LNM in prostate cancer patients. EVIDENCE ACQUISITION Cochrane, PubMed, Embase, and Web of Science were searched up to November 1, 2022. The risk of bias in the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). The concordance index (C-index), sensitivity, and specificity were adopted to evaluate the predictive accuracy of the models. RESULTS Thirty-one studies (18,803 patients) were included. Seven kinds of nomograms currently recommended, dominated by Briganti nomogram or MSKCC nomogram, were covered in the included studies. For newly-developed ML models, the C-index for LNM prediction in the training set and validation set was 0.846 [95%CI (0.818, 0.873)] and 0.862 [95%CI (0.819-0.905)] respectively. Most ML models in the training set were based on Logistic Regression (LR), which had a sensitivity of 0.78 [95%CI (0.70, 0.85)] and a specificity of 0.85 [95%CI (0.77, 0.90)] in the training set, and a sensitivity of 0.81 [95%CI (0.67, 0.89)] and a specificity of 0.82 [95%CI (0.75, 0.88)] in the validation set. For the recommended nomograms, the C-index in the validation set was 0.745 [95%CI (0.701, 0.790)] for the Briganti nomogram and 0.714 [95%CI (0.662, 0.765)] for the MSKCC nomogram. CONCLUSION The predictive accuracy of ML is superior to existing clinically recommended nomograms, and appropriate updates can be conducted to existing nomograms according to special situations.
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Affiliation(s)
- Hao Wang
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Zhongyou Xia
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Yulai Xu
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Jing Sun
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China
| | - Ji Wu
- Department of Urology, Nanchong Central Hospital, The Second Clinical College, North Sichuan Medical College (University), Nanchong, 637000, Sichuan, China.
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Huynh LM, Hwang Y, Taylor O, Baine MJ. The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature. Diagnostics (Basel) 2023; 13:diagnostics13061128. [PMID: 36980436 PMCID: PMC10047271 DOI: 10.3390/diagnostics13061128] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
The development of precise medical imaging has facilitated the establishment of radiomics, a computer-based method of quantitatively analyzing subvisual imaging characteristics. The present review summarizes the current literature on the use of diagnostic magnetic resonance imaging (MRI)-derived radiomics in prostate cancer (PCa) risk stratification. A stepwise literature search of publications from 2017 to 2022 was performed. Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. In predicting GS and adverse pathology, radiomic models differentiated well, with receiver operator characteristic area under the curve (ROC-AUC) values of 0.50–0.92 and 0.60–0.92, respectively. For studies predicting post-treatment recurrence or failure, ROC-AUC for radiomic models ranged from 0.73 to 0.99 in postsurgical and radiation cohorts. Finally, of the 33 studies, 7 (21.2%) included external validation. Overall, most investigations showed good to excellent prediction of GS and adverse pathology with MRI-derived radiomic features. Direct prediction of treatment outcomes, however, is an ongoing investigation. As these studies mature and reach potential for clinical integration, concerted effort to validate these radiomic models must be undertaken.
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Affiliation(s)
- Linda My Huynh
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
- Department of Urology, University of California, Orange, CA 92868, USA
| | - Yeagyeong Hwang
- Department of Urology, University of California, Orange, CA 92868, USA
| | - Olivia Taylor
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
| | - Michael J. Baine
- Department of Radiation Oncology, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, 987521 Nebraska Medical Center, Omaha, NE 68198-7521, USA
- Correspondence:
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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.
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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
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Liu X, Zhu Z, Wang K, Zhang Y, Li J, Wang X, Zhang X, Wang X. Semiautomated pelvic lymph node treatment response evaluation for patients with advanced prostate cancer: based on MET-RADS-P guidelines. Cancer Imaging 2023; 23:7. [PMID: 36650584 PMCID: PMC9847043 DOI: 10.1186/s40644-023-00523-4] [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: 10/04/2022] [Accepted: 01/05/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The evaluation of treatment response according to METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P) criteria is an important but time-consuming task for patients with advanced prostate cancer (APC). A deep learning-based algorithm has the potential to assist with this assessment. OBJECTIVE To develop and evaluate a deep learning-based algorithm for semiautomated treatment response assessment of pelvic lymph nodes. METHODS A total of 162 patients who had undergone at least two scans for follow-up assessment after APC metastasis treatment were enrolled. A previously reported deep learning model was used to perform automated segmentation of pelvic lymph nodes. The performance of the deep learning algorithm was evaluated using the Dice similarity coefficient (DSC) and volumetric similarity (VS). The consistency of the short diameter measurement with the radiologist was evaluated using Bland-Altman plotting. Based on the segmentation of lymph nodes, the treatment response was assessed automatically with a rule-based program according to the MET-RADS-P criteria. Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and two radiologists [attending radiologist (R1) and fellow radiologist (R2)]. RESULTS The mean DSC and VS of the pelvic lymph node segmentation were 0.82 ± 0.09 and 0.88 ± 0.12, respectively. Bland-Altman plotting showed that most of the lymph node measurements were within the upper and lower limits of agreement (LOA). The accuracies of automated segmentation-based assessment were 0.92 (95% CI: 0.85-0.96), 0.91 (95% CI: 0.86-0.95) and 75% (95% CI: 0.46-0.92) for target lesions, nontarget lesions and nonpathological lesions, respectively. The consistency of treatment response assessment based on automated segmentation and manual segmentation was excellent for target lesions [K value: 0.92 (0.86-0.98)], good for nontarget lesions [0.82 (0.74-0.90)] and moderate for nonpathological lesions [0.71 (0.50-0.92)]. CONCLUSION The deep learning-based semiautomated algorithm showed high accuracy for the treatment response assessment of pelvic lymph nodes and demonstrated comparable performance with radiologists.
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Affiliation(s)
- Xiang Liu
- grid.411472.50000 0004 1764 1621Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034 China
| | - Zemin Zhu
- grid.501248.aDepartment of Hepatobiliary and Pancreatic Surgery, Zhuzhou Central Hospital, Zhuzhou, 412000 China
| | - Kexin Wang
- grid.24696.3f0000 0004 0369 153XSchool of Basic Medical Sciences, Capital Medical University, Beijing, 100069 China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011 China
| | - Jialun Li
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011 China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011 China
| | - Xiaodong Zhang
- grid.411472.50000 0004 1764 1621Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034 China
| | - Xiaoying Wang
- grid.411472.50000 0004 1764 1621Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034 China
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