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Liu Y, Zhao L, Bao J, Hou J, Jing Z, Liu S, Li X, Cao Z, Yang B, Shen J, Zhang J, Ji L, Kang Z, Hu C, Wang L, Liu J. Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics. Vis Comput Ind Biomed Art 2024; 7:16. [PMID: 38967824 PMCID: PMC11226574 DOI: 10.1186/s42492-024-00167-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/20/2024] [Indexed: 07/06/2024] Open
Abstract
Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.
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Affiliation(s)
- Yuwei Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Litao Zhao
- 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, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Jie Bao
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China
| | - Jian Hou
- Department of CT-MR Center, the People's Hospital of Jimo, Qingdao, 266200, Shandong Province, China
| | - Zhaozhao Jing
- Department of Radiology, Sinopharm Tongmei General Hospital, Datong, 037003, Shanxi Province, China
| | - Songlu Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Xuanhao Li
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Zibing Cao
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Boyu Yang
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Junkang Shen
- Department of Radiology, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu Province, China
| | - Ji Zhang
- Department of Radiology, the People's Hospital of Taizhou, Taizhou, 225399, Jiangsu Province, China
| | - Libiao Ji
- Department of Radiology, Changshu No. 1 People's Hospital, Changshu, 215501, Jiangsu Province, China
| | - Zhen Kang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China
| | - Chunhong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, China.
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
| | - Jiangang 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, Beijing, 100191, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100191, China.
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Small C, Prior P, Nasief H, Zeitlin R, Saeed H, Paulson E, Morrow N, Rownd J, Erickson B, Bedi M. A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer. Brachytherapy 2023; 22:728-735. [PMID: 37574352 DOI: 10.1016/j.brachy.2023.06.004] [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: 02/27/2023] [Revised: 05/11/2023] [Accepted: 06/06/2023] [Indexed: 08/15/2023]
Abstract
PURPOSE Treatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS). MATERIALS AND METHODS A retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed. Clinical factors together with 18 radiomic features extracted from different radiomics matrices were analyzed. The delta radiomic features (DRFs) were extracted from MRI on the first and last brachytherapy fractions. Support Vector Machine (SVM) models were fitted to combinations of 2-3 DRFs found significant after Spearman correlation and Wilcoxon rank sum test statistics. Additional models were tested that included clinical factors together with DRFs. RESULTS A total of 39 patients were included in the analysis with a median patient age of 52 years. Progression occurred in 20% of patients (8/39). The significant DRFs using two DRF feature combinations was a model using auto correlation (AC) and sum variance (SV). The best performing three feature model combined mean, AC & SV. Additionally, the inclusion of FIGO stages with the 2- and 3 DRF combination model(s) improved performance compared to models with only DRFs. However, all the clinical factor + DRF models were not significantly different from one another (all AUCs were 0.77). CONCLUSIONS Our study shows promising evidence that radiomics metrics are associated with progression free survival in cervical cancer.
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Affiliation(s)
- Christina Small
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI.
| | - Phillip Prior
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Haidy Nasief
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Ross Zeitlin
- Department of Radiation Oncology, John H Stroger, Jr. Hospital of Cook County, Chicago, IL
| | - Hina Saeed
- Department of Radiation Oncology, Lynn Cancer Institute, Baptist Health South Florida, Boynton Beach, FL
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Natalya Morrow
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Jason Rownd
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Meena Bedi
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
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Bizzarri N, Russo L, Dolciami M, Zormpas-Petridis K, Boldrini L, Querleu D, Ferrandina G, Pedone Anchora L, Gui B, Sala E, Scambia G. Radiomics systematic review in cervical cancer: gynecological oncologists' perspective. Int J Gynecol Cancer 2023; 33:1522-1541. [PMID: 37714669 DOI: 10.1136/ijgc-2023-004589] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2023] Open
Abstract
OBJECTIVE Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. METHODS A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. RESULTS A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. CONCLUSION Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.
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Affiliation(s)
- Nicolò Bizzarri
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Russo
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Miriam Dolciami
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Konstantinos Zormpas-Petridis
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Denis Querleu
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gabriella Ferrandina
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luigi Pedone Anchora
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evis Sala
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Scambia
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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Zhang Y, Liu L, Zhang K, Su R, Jia H, Qian L, Dong J. Nomograms Combining Clinical and Imaging Parameters to Predict Recurrence and Disease-free Survival After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer. Acad Radiol 2023; 30:499-508. [PMID: 36050264 DOI: 10.1016/j.acra.2022.08.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/31/2022] [Accepted: 08/01/2022] [Indexed: 01/27/2023]
Abstract
PURPOSES To investigate the value of nomograms based on clinical prognostic factors (CPF), intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and MRI-derived radiomics in predicting recurrence and disease-free survival (DFS) after concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer (LACC). METHODS Retrospective analysis of data from 115 patients with ⅠB-ⅣA cervical cancer who underwent CCRT and had been followed up consistently. All patients were randomized 2:1 into training and validation groups. Pre-treatment IVIM-DWI parameters (ADC-value, D-value, D*-value and f-value) and pre- and post-treatment three-dimensional radiomics parameters (from axial T2WI) of primary lesions were measured. The LASSO algorithm and Logistic regression analysis were used to filter texture features and calculate radiomics score (Rad-score). Multivariate Logistic and Cox regression analysis was used to construct nomograms to predict recurrence and DFS for patients with LACC after CCRT respectively, with internal and external validation. RESULTS External beam radiotherapy dose, f-value, pre-treatment and post-treatment Rad-score were independent prognostic factors for recurrence and DFS in patients with cervical cancer, forming Model1 and Model2, with OR values of 0.480, 1.318, 3.071, 3.200 and HR values of 0.322, 3.372, 5.138, 7.204. The area under the curve (AUC) of Model1 for predicting recurrence of cervical cancer was 0.977, with internal and external validation C-indexes of 0.977 and 0.962. The AUC for Model2 predicting disease-free survival (DFS) at 1, 3, and 5 years was 0.895, 0.888 and 0.916 respectively, with internal and external C-indexes of 0.860 and 0.892. The decision curves analysis and clinical impact curves further indicate the high predictive efficiency and stability of nomograms. CONCLUSION The nomograms based on clinical, IVIM-DWI and radiomics parameters have high clinical value in predicting recurrence and DFS of patients with LACC after CCRT and can provide a reference for prognostic assessment and individualized treatment of cervical cancer patients.
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Affiliation(s)
- Yu Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Long Liu
- Department of Hepatobiliary Surgery, Taizhou Hospital of Zhejiang University, Taizhou, Zhejiang, China
| | - Kaiyue Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Rixin Su
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Haodong Jia
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China; Department of Radiology, the First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China
| | - Liting Qian
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China
| | - Jiangning Dong
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230001, China; Department of Radiology, the First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China.
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