1
|
Şam Özdemir M, Azamat S, Özdemir H, Keskin ET, Savun M, Şimşek A, Yardımcı AH. Preoperative Prediction of Muscle Invasiveness in Bladder Cancer: The Role of 3D Volumetric Radiomics Using Diffusion-Weighted MRI, the VI-RADS Score, or a Combination of Both. Ann Surg Oncol 2024; 31:5845-5850. [PMID: 39003377 DOI: 10.1245/s10434-024-15760-5] [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: 04/12/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Bladder cancer treatment decisions hinge on detecting muscle invasion. The 2018 "Vesical Imaging Reporting and Data System" (VI-RADS) standardizes multiparametric MRI (mp-MRI) use. Radiomics, an analysis framework, provides more insightful information than conventional methods. PURPOSE To determine how well MIBC (Muscle Invasive Bladder Cancer) and NMIBC (Non-Muscle Invasive Bladder Cancer) can be distinguished using mp-MRI radiomics features. METHODS We conducted a study with 73 bladder cancer patients diagnosed pathologically, who underwent preoperative mp-MRI from January 2020 to July 2022. Utilizing 3D Slicer (version 4.8.1) and Pyradiomics, we manually extracted radiomic features from apparent diffusion coefficient (ADC) maps created from diffusion-weighted imaging. The LASSO approach identified optimal features, and we addressed sample imbalance using SMOTE. We developed a classification model using textural features alone or combined with VI-RADS, employing a random forest classifier with 10-fold cross-validation. Diagnostic performance was assessed using the area under the ROC curve analysis. RESULTS Among 73 patients (63 men, 10 women; median age: 63 years), 41 had muscle-invasive and 32 had superficial bladder cancer. Muscle invasion was observed in 25 of 41 patients with VI-RADS 4 and 5 scores and 12 of 32 patients with VI-RADS 1, 2, and 3 scores (accuracy: 77.5%, sensitivity: 67.7%, specificity: 88.8%). The combined VI-RADS score and radiomics model (AUC = 0.92 ± 0.12) outperformed the single radiomics model using ADC MRI (AUC = 0.83 ± 0.22 with 10-fold cross-validation) in this dataset. CONCLUSION Before undergoing surgery, bladder cancer invasion in muscle might potentially be predicted using a radiomics signature based on mp-MRI.
Collapse
Affiliation(s)
- Merve Şam Özdemir
- Department of Radiology, Başaksehir Çam and Sakura City Hospital, Istanbul, Turkey.
| | - Sena Azamat
- Department of Radiology, Başaksehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Harun Özdemir
- Department of Urology, Başaksehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Emin Taha Keskin
- Department of Urology, Başaksehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Metin Savun
- Department of Urology, Başaksehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Abdulmuttalip Şimşek
- Department of Urology, Başaksehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Aytül Hande Yardımcı
- Department of Radiology, Başaksehir Çam and Sakura City Hospital, Istanbul, Turkey
| |
Collapse
|
2
|
He C, Xu H, Yuan E, Ye L, Chen Y, Yao J, Song B. The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction. Insights Imaging 2024; 15:185. [PMID: 39090234 PMCID: PMC11294512 DOI: 10.1186/s13244-024-01780-y] [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: 04/09/2024] [Accepted: 07/10/2024] [Indexed: 08/04/2024] Open
Abstract
PURPOSE To evaluate the diagnostic performance of image-based artificial intelligence (AI) studies in predicting muscle-invasive bladder cancer (MIBC). (2) To assess the reporting quality and methodological quality of these studies by Checklist for Artificial Intelligence in Medical Imaging (CLAIM), Radiomics Quality Score (RQS), and Prediction model Risk of Bias Assessment Tool (PROBAST). MATERIALS AND METHODS We searched Medline, Embase, Web of Science, and The Cochrane Library databases up to October 30, 2023. The eligible studies were evaluated using CLAIM, RQS, and PROBAST. Pooled sensitivity, specificity, and the diagnostic performances of these models for MIBC were also calculated. RESULTS Twenty-one studies containing 4256 patients were included, of which 17 studies were employed for the quantitative statistical analysis. The CLAIM study adherence rate ranged from 52.5% to 75%, with a median of 64.1%. The RQS points of each study ranged from 2.78% to 50% points, with a median of 30.56% points. All models were rated as high overall ROB. The pooled area under the curve was 0.85 (95% confidence interval (CI) 0.81-0.88) for computed tomography, 0.92 (95% CI 0.89-0.94) for MRI, 0.89 (95% CI 0.86-0.92) for radiomics and 0.91 (95% CI 0.88-0.93) for deep learning, respectively. CONCLUSION Although AI-powered muscle-invasive bladder cancer-predictive models showed promising performance in the meta-analysis, the reporting quality and the methodological quality were generally low, with a high risk of bias. CRITICAL RELEVANCE STATEMENT Artificial intelligence might improve the management of patients with bladder cancer. Multiple models for muscle-invasive bladder cancer prediction were developed. Quality assessment is needed to promote clinical application. KEY POINTS Image-based artificial intelligence models could aid in the identification of muscle-invasive bladder cancer. Current studies had low reporting quality, low methodological quality, and a high risk of bias. Future studies could focus on larger sample sizes and more transparent reporting of pathological evaluation, model explanation, and failure and sensitivity analyses.
Collapse
Affiliation(s)
- Chunlei He
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Lei Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China.
| |
Collapse
|
3
|
Xing X, Li L, Sun M, Yang J, Zhu X, Peng F, Du J, Feng Y. Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma. Heliyon 2024; 10:e34163. [PMID: 39071606 PMCID: PMC11279278 DOI: 10.1016/j.heliyon.2024.e34163] [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: 01/30/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024] Open
Abstract
Objective Invasive lung adenocarcinoma(ILA) with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution(SR) enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning. Methods Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC). Result A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility. Conclusion The study successfully constructed and evaluated a deep learning(DL)-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.
Collapse
Affiliation(s)
- Xiaowei Xing
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liangping Li
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Mingxia Sun
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Jiahu Yang
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Xinhai Zhu
- Department of Thoracic Surgery, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Fang Peng
- Department of Pathology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Jianzong Du
- Department of Respiratory Medicine, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Yue Feng
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| |
Collapse
|
4
|
Li S, Fan Z, Guo J, Li D, Chen Z, Zhang X, Wang Y, Li Y, Yang G, Wang X. Compressed sensing 3D T2WI radiomics model: improving diagnostic performance in muscle invasion of bladder cancer. BMC Med Imaging 2024; 24:148. [PMID: 38886638 PMCID: PMC11181529 DOI: 10.1186/s12880-024-01318-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Preoperative discrimination between non-muscle-invasive bladder cancer (NMIBC) and the muscle invasive bladder cancer (MIBC) is a determinant of management. The purpose of this research is to employ radiomics to evaluate the diagnostic value in determining muscle invasiveness of compressed sensing (CS) accelerated 3D T2-weighted-SPACE sequence with high resolution and short acquisition time. METHODS This prospective study involved 108 participants who underwent preoperative 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted sequences. The cohort was divided into training and validation cohorts in a 7:3 ratio. In the training cohort, a Rad-score was constructed based on radiomic features selected by intraclass correlation coefficients, pearson correlation coefficient and least absolute shrinkage and selection operator . Multivariate logistic regression was used to develop a nomogram combined radiomics and clinical indices. In the validation cohort, the performances of the models were evaluated by ROC, calibration, and decision curves. RESULTS In the validation cohort, the area under ROC curve of 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted models were 0.87(95% confidence interval (CI):0.73-1.00), 0.79(95%CI:0.63-0.96) and 0.77(95%CI:0.60-0.93), respectively. The differences in signal-to-noise ratio and contrast-to-noise ratio between 3D-CS-T2-weighted-SPACE and 3D-T2-weighted-SPACE sequences were not statistically significant(p > 0.05). While the clinical model composed of three clinical indices was 0.74(95%CI:0.55-0.94) and the radiomics-clinical nomogram model was 0.88(95%CI:0.75-1.00). The calibration curves confirmed high goodness of fit, and the decision curve also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. CONCLUSION The radiomics model based on compressed sensing 3D T2WI sequence, which was acquired within a shorter acquisition time, showed superior diagnostic efficacy in muscle invasion of bladder cancer. Additionally, the nomogram model could enhance the diagnostic performance.
Collapse
Affiliation(s)
- Shuo Li
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Zhichang Fan
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Junting Guo
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Ding Li
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Zeke Chen
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Xiaoyue Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Yongfang Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Yan Li
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Guoqiang Yang
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Xiaochun Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China.
| |
Collapse
|
5
|
Liu L, Xu L, Wu D, Zhu Y, Li X, Xu C, Chen K, Lin Y, Lao J, Cai P, Li X, Luo Y, Li X, Huang J, Lin T, Zhong W. Impact of tumour stroma-immune interactions on survival prognosis and response to neoadjuvant chemotherapy in bladder cancer. EBioMedicine 2024; 104:105152. [PMID: 38728838 PMCID: PMC11090066 DOI: 10.1016/j.ebiom.2024.105152] [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: 11/29/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND The tumour stroma is associated with unfavourable prognosis in diverse solid tumours, but its prognostic and predictive value in bladder cancer (BCa) is unclear. METHODS In this multicentre, retrospective study, we included 830 patients with BCa from six independent cohorts. Differences in overall survival (OS) and cancer-specific survival (CSS) were investigated between high-tumour stroma ratio (TSR) and low-TSR groups. Multi-omics analyses, including RNA sequencing, immunohistochemistry, and single-cell RNA sequencing, were performed to study stroma-immune interactions. TSR prediction models were developed based on pelvic CT scans, and the best performing model was selected based on receiver operator characteristic analysis. FINDINGS Compared to low-TSR tumours, high-TSR tumours were significantly associated with worse OS (HR = 1.193, 95% CI: 1.046-1.361, P = 0.008) and CSS (HR = 1.337, 95% CI: 1.139-1.569, P < 0.001), and lower rate of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). High-TSR tumours exhibited higher infiltration of immunosuppressive cells, including Tregs and tumour-associated neutrophils, while low-TSR tumours exhibited higher infiltration of immune-activating cells such as CD8+ Teff and XCR1+ dendritic cells. The TSR prediction model was developed by combining the intra-tumour and tumour base radiomics features, and showed good performance to predict high-TSR, as indicted by area under the curve of 0.871 (95% CI: 0.821-0.921), 0.821 (95% CI: 0.731-0.911), and 0.801 (95% CI: 0.737-0.865) in the training, internal validation, and external validation cohorts, respectively. In patients with low predicted TSR, 92.3% (12/13) achieved pCR, while only 35.3% (6/17) of patients with high predicted TSR achieved pCR. INTERPRETATION The tumour stroma was found to be significantly associated with clinical outcomes in patients with BCa as a result of tumour stroma-immune interactions. The radiomics prediction model provided non-invasive evaluation of TSR and was able to predict pCR in patients receiving NAC for BCa. FUNDING This work was supported by National Natural Science Foundation of China (Grant No. 82373254 and 81961128027), Guangdong Provincial Natural Science Foundation (Grant No. 2023A1515010258), Science and Technology Planning Project of Guangdong Province (Grant No. 2023B1212060013). Science and Technology Program of Guangzhou (SL2022A04J01754), Sun Yat-Sen Memorial Hospital Clinical Research 5010 Program (Grant No. SYS-5010Z-202401).
Collapse
Affiliation(s)
- Libo Liu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China
| | - Longhao Xu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China
| | - Daqin Wu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China
| | - Yingying Zhu
- Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Xiaoyang Li
- Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Chunru Xu
- Department of Urology, Peking University First Hospital, Beijing, PR China
| | - Ke Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China
| | - Yi Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China
| | - Jianwen Lao
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China
| | - Peicong Cai
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China
| | - Xuesong Li
- Department of Urology, Peking University First Hospital, Beijing, PR China
| | - Yun Luo
- Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Xiang Li
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Jian Huang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China.
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China.
| | - Wenlong Zhong
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China; Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, PR China.
| |
Collapse
|
6
|
Zheng J, Zhang J, Cai J, Yao Y, Lu S, Wu Z, Cai Z, Tuerxun A, Batur J, Huang J, Kong J, Lin T. Development of a radiomics model to discriminate ammonium urate stones from uric acid stones in vivo : A remedy for the diagnostic pitfall of dual-energy computed tomography. Chin Med J (Engl) 2024; 137:1095-1104. [PMID: 37994499 PMCID: PMC11062676 DOI: 10.1097/cm9.0000000000002866] [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: 06/05/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them. METHODS This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated. RESULTS When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899-0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796-0.995) and 0.870 (95% CI, 0.769-0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model. CONCLUSIONS DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo .
Collapse
Affiliation(s)
- Junjiong Zheng
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong 510120, China
| | - Jie Zhang
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong 510120, China
| | - Jinhua Cai
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510120, China
| | - Yuhui Yao
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong 510120, China
| | - Sihong Lu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong 510120, China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510120, China
| | - Zhaoxi Cai
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510120, China
| | - Aierken Tuerxun
- Department of Urology, The First People's Hospital of Kashgar Prefecture, Kashgar, Xinjiang 844000, China
| | - Jesur Batur
- Department of Urology, The First People's Hospital of Kashgar Prefecture, Kashgar, Xinjiang 844000, China
| | - Jian Huang
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong 510120, China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong 510120, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong 510120, China
| |
Collapse
|
7
|
Zhang R, Jia S, Zhai L, Wu F, Zhang S, Li F. Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram. BMC Med Imaging 2024; 24:98. [PMID: 38678222 PMCID: PMC11055285 DOI: 10.1186/s12880-024-01276-7] [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: 11/11/2023] [Accepted: 04/19/2024] [Indexed: 04/29/2024] Open
Abstract
OBJECTIVES The aim of the study is to assess the efficacy of the established computed tomography (CT)-based radiomics nomogram combined with radiomics and clinical features for predicting muscle invasion status in bladder cancer (BCa). METHODS A retrospective analysis was conducted using data from patients who underwent CT urography at our institution between May 2018 and April 2023 with urothelial carcinoma of the bladder confirmed by postoperative histology. There were 196 patients enrolled in all, and each was randomized at random to either the training cohort (n = 137) or the test cohort (n = 59). Eight hundred fifty-one radiomics features in all were retrieved. For feature selection, the significance test and least absolute shrinkage and selection operator (LASSO) approaches were utilized. Subsequently, the radiomics score (Radscore) was obtained by applying linear weighting based on the selected features. The clinical and radiomics model, as well as radiomics-clinical nomogram were all established using logistic regression. Three models were evaluated using analysis of the receiver operating characteristic curve. An area under the curve (AUC) and 95% confidence intervals (CI) as well as specificity, sensitivity, accuracy, negative predictive value, and positive predictive value were included in the analysis. Radiomics-clinical nomogram's performance was assessed based on discrimination, calibration, and clinical utility. RESULTS After obtaining 851 radiomics features, 12 features were ultimately selected. Histopathological grading and tortuous blood vessels were included in the clinical model. The Radscore and clinical histopathology grading were among the final predictors in the unique nomogram. The three models had an AUC of 0.811 (95% CI, 0.742-0.880), 0.845 (95% CI, 0.781-0.908), and 0.896 (95% CI, 0.846-0.947) in the training cohort and in the test cohort they were 0.808 (95% CI, 0.703-0.913), 0.847 (95% CI, 0.739-0.954), and 0.887 (95% CI, 0.803-0.971). According to the DeLong test, the radiomics-clinical nomogram's AUC in the training cohort substantially differed from that of the clinical model (AUC: 0.896 versus 0.845, p = 0.015) and the radiomics model (AUC: 0.896 versus 0.811, p = 0.002). The Delong test in the test cohort revealed no significant difference among the three models. CONCLUSIONS CT-based radiomics-clinical nomogram can be a useful tool for quantitatively predicting the status of muscle invasion in BCa.
Collapse
Affiliation(s)
- Rui Zhang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Shijun Jia
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Linhan Zhai
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Feng Wu
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Shuang Zhang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China.
| | - Feng Li
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China.
| |
Collapse
|
8
|
Fang F, Wu L, Luo X, Bu H, Huang Y, Xian Wu Y, Lu Z, Li T, Yang G, Zhao Y, Weng H, Zhao J, Ma C, Li C. Differentiation of testicular seminomas from nonseminomas based on multiphase CT radiomics combined with machine learning: A multicenter study. Eur J Radiol 2024; 175:111416. [PMID: 38460443 DOI: 10.1016/j.ejrad.2024.111416] [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: 10/19/2023] [Revised: 02/26/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
BACKGROUND Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose. METHODS In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility. RESULTS Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas. CONCLUSION The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.
Collapse
Affiliation(s)
- Fuxiang Fang
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Linfeng Wu
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Xing Luo
- Department of Urology, Baise People's Hospital, Baise 533099, China.
| | - Huiping Bu
- Department of Radiology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Yueting Huang
- Department of Epidemiology and Health Statistics, School of Public Health of Guangxi Medical University, Nanning 530021, China.
| | - Yong Xian Wu
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Zheng Lu
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Tianyu Li
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Guanglin Yang
- Department of Urology, Affiliated Cancer Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Yutong Zhao
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Hongchao Weng
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Jiawen Zhao
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Chenjun Ma
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Chengyang Li
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| |
Collapse
|
9
|
Huang J, Chen G, Liu H, Jiang W, Mai S, Zhang L, Zeng H, Wu S, Chen CYC, Wu Z. MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma. Eur Radiol 2024; 34:1804-1815. [PMID: 37658139 DOI: 10.1007/s00330-023-10137-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVES It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC. METHODS A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model. RESULTS This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000). CONCLUSION The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC. CLINICAL RELEVANCE STATEMENT This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making. KEY POINTS • It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.
Collapse
Affiliation(s)
- Jingwen Huang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, China
| | - Haiqing Liu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Wei Jiang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Siyao Mai
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Lingli Zhang
- Department of Pathology, Shenshan Medical Center, Memorial Hospital of Sun Yat-Sen University, Shanwei, 516600, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510120, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, China.
- Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan.
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan.
| | - Zhuo Wu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510120, China.
| |
Collapse
|
10
|
He K, Meng X, Wang Y, Feng C, Liu Z, Li Z, Niu Y. Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review. Diagnostics (Basel) 2024; 14:442. [PMID: 38396481 PMCID: PMC10888296 DOI: 10.3390/diagnostics14040442] [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: 12/21/2023] [Revised: 01/25/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.
Collapse
Affiliation(s)
- Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Zheng Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Yonghua Niu
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| |
Collapse
|
11
|
Wei Z, Liu H, Xv Y, Liao F, He Q, Xie Y, Lv F, Jiang Q, Xiao M. Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer. Heliyon 2024; 10:e24878. [PMID: 38304824 PMCID: PMC10831750 DOI: 10.1016/j.heliyon.2024.e24878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
Objective This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813-0.953) in internal validation cohort and 0.862 (95 % CI: 0.756-0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.
Collapse
Affiliation(s)
- Zongjie Wei
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huayun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yingjie Xv
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fangtong Liao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Quanhao He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yongpeng Xie
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
12
|
Song H, Yang S, Yu B, Li N, Huang Y, Sun R, Wang B, Nie P, Hou F, Huang C, Zhang M, Wang H. CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study. Cancer Imaging 2023; 23:89. [PMID: 37723572 PMCID: PMC10507832 DOI: 10.1186/s40644-023-00609-z] [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: 05/27/2023] [Accepted: 09/10/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively. METHODS We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature. CONCLUSION The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.
Collapse
Affiliation(s)
- Hongzheng Song
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Boyang Yu
- Qingdao No.58 High School of Shandong Province, Qingdao, Shandong, China
| | - Na Li
- Department of Radiology, The People's Hospital of Zhangqiu Area, Jinan, Shandong, China
| | - Yonghua Huang
- Department of Radiology, The Puyang Oilfield General Hospital, Puyang, Henan, China
| | - Rui Sun
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Bo Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China.
| |
Collapse
|
13
|
Qiu J, Yan M, Wang H, Liu Z, Wang G, Wu X, Gao Q, Hu H, Chen J, Dai Y. Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study. Front Med (Lausanne) 2023; 10:1202486. [PMID: 37601775 PMCID: PMC10433756 DOI: 10.3389/fmed.2023.1202486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/12/2023] [Indexed: 08/22/2023] Open
Abstract
Obstructive To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics. Methods A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models. Results 1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722-0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models. Conclusion Machine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.
Collapse
Affiliation(s)
- Junliang Qiu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Minbo Yan
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Haojie Wang
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Zicheng Liu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Guojie Wang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Xianbo Wu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Qindong Gao
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Hongji Hu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Junyong Chen
- Department of Urology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, Guangdong, China
| | - Yingbo Dai
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| |
Collapse
|
14
|
Ferro M, Falagario UG, Barone B, Maggi M, Crocetto F, Busetto GM, Giudice FD, Terracciano D, Lucarelli G, Lasorsa F, Catellani M, Brescia A, Mistretta FA, Luzzago S, Piccinelli ML, Vartolomei MD, Jereczek-Fossa BA, Musi G, Montanari E, Cobelli OD, Tataru OS. Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement. Diagnostics (Basel) 2023; 13:2308. [PMID: 37443700 DOI: 10.3390/diagnostics13132308] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.
Collapse
Affiliation(s)
- Matteo Ferro
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Ugo Giovanni Falagario
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Biagio Barone
- Urology Unit, Department of Surgical Sciences, AORN Sant'Anna e San Sebastiano, 81100 Caserta, Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Francesco Del Giudice
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Michele Catellani
- Department of Urology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Antonio Brescia
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Stefano Luzzago
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Mattia Luca Piccinelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | | | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Division of Radiation Oncology, IEO-European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gennaro Musi
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Octavian Sabin Tataru
- Department of Simulation Applied in Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mures, 540142 Târgu Mures, Romania
| |
Collapse
|
15
|
Boca B, Caraiani C, Telecan T, Pintican R, Lebovici A, Andras I, Crisan N, Pavel A, Diosan L, Balint Z, Lupsor-Platon M, Buruian MM. MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment. Diagnostics (Basel) 2023; 13:2300. [PMID: 37443692 DOI: 10.3390/diagnostics13132300] [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: 05/22/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
(1): Background: With the recent introduction of vesical imaging reporting and data system (VI-RADS), magnetic resonance imaging (MRI) has become the main imaging method used for the preoperative local staging of bladder cancer (BCa). However, the VI-RADS score is subject to interobserver variability and cannot provide information about tumor cellularity. These limitations may be overcome by using a quantitative approach, such as the new emerging domain of radiomics. (2) Aim: To systematically review published studies on the use of MRI-based radiomics in bladder cancer. (3) Materials and Methods: We performed literature research using the PubMed MEDLINE, Scopus, and Web of Science databases using PRISMA principles. A total of 1092 papers that addressed the use of radiomics for BC staging, grading, and treatment response were retrieved using the keywords "bladder cancer", "magnetic resonance imaging", "radiomics", and "textural analysis". (4) Results: 26 papers met the eligibility criteria and were included in the final review. The principal applications of radiomics were preoperative tumor staging (n = 13), preoperative prediction of tumor grade or molecular correlates (n = 9), and prediction of prognosis/response to neoadjuvant therapy (n = 4). Most of the developed radiomics models included second-order features mainly derived from filtered images. These models were validated in 16 studies. The average radiomics quality score was 11.7, ranging between 8.33% and 52.77%. (5) Conclusions: MRI-based radiomics holds promise as a quantitative imaging biomarker of BCa characterization and prognosis. However, there is still need for improving the standardization of image preprocessing, feature extraction, and external validation before applying radiomics models in the clinical setting.
Collapse
Affiliation(s)
- Bianca Boca
- Department of Radiology, "George Emil Palade", University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
- Department of Medical Imaging and Nuclear Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Cosmin Caraiani
- Department of Medical Imaging and Nuclear Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Teodora Telecan
- Department of Urology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Urology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Roxana Pintican
- Department of Radiology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Andrei Lebovici
- Department of Radiology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Iulia Andras
- Department of Urology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Urology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Nicolae Crisan
- Department of Urology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Urology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Alexandru Pavel
- Department of Radiology, "George Emil Palade", University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Laura Diosan
- Department of Computer Science, Faculty of Mathematics and Computer Science, "Babes-Bolyai" University, 400157 Cluj-Napoca, Romania
| | - Zoltan Balint
- Department of Biomedical Physics, Faculty of Physics, "Babes-Bolyai" University, 400084 Cluj-Napoca, Romania
| | - Monica Lupsor-Platon
- Department of Medical Imaging and Nuclear Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Radiology, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. Octavian Fodor", 400162 Cluj-Napoca, Romania
| | - Mircea Marian Buruian
- Department of Radiology, "George Emil Palade", University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| |
Collapse
|
16
|
Pan J, Hong G, Zeng H, Liao C, Li H, Yao Y, Gan Q, Wang Y, Wu S, Lin T. An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer. J Transl Med 2023; 21:42. [PMID: 36691055 PMCID: PMC9869632 DOI: 10.1186/s12967-023-03888-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 01/12/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Accurate pathological diagnosis of invasion depth and histologic grade is key for clinical management in patients with bladder cancer (BCa), but it is labour-intensive, experience-dependent and subject to interobserver variability. Here, we aimed to develop a pathological artificial intelligence diagnostic model (PAIDM) for BCa diagnosis. METHODS A total of 854 whole slide images (WSIs) from 692 patients were included and divided into training and validation sets. The PAIDM was developed using the training set based on the deep learning algorithm ScanNet, and the performance was verified at the patch level in validation set 1 and at the WSI level in validation set 2. An independent validation cohort (validation set 3) was employed to compare the PAIDM and pathologists. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. RESULTS The AUCs of the PAIDM were 0.878 (95% CI 0.875-0.881) at the patch level in validation set 1 and 0.870 (95% CI 0.805-0.923) at the WSI level in validation set 2. In comparing the PAIDM and pathologists, the PAIDM achieved an AUC of 0.847 (95% CI 0.779-0.905), which was non-inferior to the average diagnostic level of pathologists. There was high consistency between the model-predicted and manually annotated areas, improving the PAIDM's interpretability. CONCLUSIONS We reported an artificial intelligence-based diagnostic model for BCa that performed well in identifying invasion depth and histologic grade. Importantly, the PAIDM performed admirably in patch-level recognition, with a promising application for transurethral resection specimens.
Collapse
Affiliation(s)
- Jiexin Pan
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guibin Hong
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China
| | - Huarun Li
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China
| | - Yuhui Yao
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China
| | - Qinghua Gan
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China
| | - Yun Wang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong, China.
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong, China.
| |
Collapse
|
17
|
Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study. Sci Rep 2023; 13:628. [PMID: 36635425 PMCID: PMC9837183 DOI: 10.1038/s41598-023-27883-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
This study aimed to develop a versatile automatic segmentation model of bladder cancer (BC) on MRI using a convolutional neural network and investigate the robustness of radiomics features automatically extracted from apparent diffusion coefficient (ADC) maps. This two-center retrospective study used multi-vendor MR units and included 170 patients with BC, of whom 140 were assigned to training datasets for the modified U-net model with five-fold cross-validation and 30 to test datasets for assessment of segmentation performance and reproducibility of automatically extracted radiomics features. For model input data, diffusion-weighted images with b = 0 and 1000 s/mm2, ADC maps, and multi-sequence images (b0-b1000-ADC maps) were used. Segmentation accuracy was compared between ours and existing models. The reproducibility of radiomics features on ADC maps was evaluated using intraclass correlation coefficient. The model with multi-sequence images achieved the highest Dice similarity coefficient (DSC) with five-fold cross-validation (mean DSC = 0.83 and 0.79 for the training and validation datasets, respectively). The median (interquartile range) DSC of the test dataset model was 0.81 (0.70-0.88). Radiomics features extracted from manually and automatically segmented BC exhibited good reproducibility. Thus, our U-net model performed highly accurate segmentation of BC, and radiomics features extracted from the automatic segmentation results exhibited high reproducibility.
Collapse
|
18
|
Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer. Eur Urol Focus 2023; 9:145-153. [PMID: 36115774 DOI: 10.1016/j.euf.2022.08.015] [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: 06/08/2022] [Revised: 07/26/2022] [Accepted: 08/30/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited. OBJECTIVE To evaluate the performance of quantitative radiomics signatures for detection of LN metastases in BC. DESIGN, SETTING, AND PARTICIPANTS Of 1354 patients with BC who underwent radical cystectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automatically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detection of LN metastases in BC. Statistical analysis was performed using the receiver operating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC). RESULTS AND LIMITATIONS In total, 1845 LNs were manually segmented. Automated segmentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63). CONCLUSIONS A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach. PATIENT SUMMARY For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options.
Collapse
|
19
|
de Celis Alonso B. Editorial for “Multi‐Sequence and Multi‐Regional
MRI
‐Based Radiomics Nomogram for the Preoperative Prediction of Muscle Invasion in Bladder Cancer”. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Benito de Celis Alonso
- Faculty of Physical and Mathematical Sciences, Medical Physics Department Benemérita Universidad Autónoma de Puebla (BUAP) Puebla Mexico
| |
Collapse
|
20
|
Zhang L, Li X, Yang L, Tang Y, Guo J, Li D, Li S, Li Y, Wang L, Lei Y, Qiao H, Yang G, Wang X. Multi‐Sequence and Multi‐Regional
MRI
‐Based Radiomics Nomogram for the Preoperative Assessment of Muscle Invasion in Bladder Cancer. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Lu Zhang
- College of Medical Imaging Shanxi Medical University Taiyuan China
| | - Xiaoyang Li
- College of Medical Imaging Shanxi Medical University Taiyuan China
| | - Li Yang
- College of Medical Imaging Shanxi Medical University Taiyuan China
| | - Ying Tang
- College of Medical Imaging Shanxi Medical University Taiyuan China
| | - Junting Guo
- College of Medical Imaging Shanxi Medical University Taiyuan China
| | - Ding Li
- College of Medical Imaging Shanxi Medical University Taiyuan China
| | - Shuo Li
- College of Medical Imaging Shanxi Medical University Taiyuan China
| | - Yan Li
- Department of Radiology The First Hospital of Shanxi Medical University Taiyuan China
| | - Le Wang
- Department of Radiology The First Hospital of Shanxi Medical University Taiyuan China
| | - Ying Lei
- Department of Radiology The First Hospital of Shanxi Medical University Taiyuan China
| | - Hong Qiao
- Department of Radiology The First Hospital of Shanxi Medical University Taiyuan China
| | - Guoqiang Yang
- Department of Radiology The First Hospital of Shanxi Medical University Taiyuan China
| | - Xiaochun Wang
- Department of Radiology The First Hospital of Shanxi Medical University Taiyuan China
| |
Collapse
|
21
|
Woźnicki P, Laqua FC, Messmer K, Kunz WG, Stief C, Nörenberg D, Schreier A, Wójcik J, Ruebenthaler J, Ingrisch M, Ricke J, Buchner A, Schulz GB, Gresser E. Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy. Cancers (Basel) 2022; 14:4449. [PMID: 36139609 PMCID: PMC9497387 DOI: 10.3390/cancers14184449] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.
Collapse
Affiliation(s)
- Piotr Woźnicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg-Oberdürrbacher Str. 6, 97080 Würzburg, Germany
| | - Fabian Christopher Laqua
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg-Oberdürrbacher Str. 6, 97080 Würzburg, Germany
| | - Katharina Messmer
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Wolfgang Gerhard Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Christian Stief
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim-Theodor-Kutzer-Ufer 1–3, 68167 Mannheim, Germany
| | - Andrea Schreier
- Department of Otolaryngology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Jan Wójcik
- Faculty of Medicine, Medical University of Warsaw, Żwirki i Wigury 61, 02091 Warsaw, Poland
| | - Johannes Ruebenthaler
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Alexander Buchner
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Gerald Bastian Schulz
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Eva Gresser
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| |
Collapse
|
22
|
Huang X, Wang X, Lan X, Deng J, Lei Y, Lin F. The role of radiomics with machine learning in the prediction of muscle-invasive bladder cancer: A mini review. Front Oncol 2022; 12:990176. [PMID: 36059618 PMCID: PMC9428259 DOI: 10.3389/fonc.2022.990176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Bladder cancer is a common malignant tumor in the urinary system. Depending on whether bladder cancer invades muscle tissue, it is classified into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). It is crucial to accurately diagnose the muscle invasion of bladder cancer for its clinical management. Although imaging modalities such as CT and multiparametric MRI play an important role in this regard, radiomics has shown great potential with the development and innovation of precision medicine. It features outstanding advantages such as non-invasive and high efficiency, and takes on important significance in tumor assessment and laor liberation. In this article, we provide an overview of radiomics in the prediction of muscle-invasive bladder cancer and reflect on its future trends and challenges.
Collapse
Affiliation(s)
- Xiaodan Huang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Xiangyu Wang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Xinxin Lan
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Jinhuan Deng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| |
Collapse
|
23
|
Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer. Eur Radiol 2022; 33:1-10. [PMID: 35726100 DOI: 10.1007/s00330-022-08952-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/30/2022] [Accepted: 06/08/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC). METHODS Seven hundred six eligible RC patients (T1/2 = 287, T3/4 = 419) were retrospectively enrolled in this study and chronologically allocated into a training cohort (n = 565) and a validation cohort (n = 141). We conducted a deep-transfer-learning network on high-resolution (HR) T2-weighted imaging (T2WI) to enhance the z-resolution of the images and acquired the preoperative SRT2WI. The radiomics models named modelHRT2 and modelSRT2 were respectively constructed with high-dimensional quantitative features extracted from manually segmented volume of interests of HRT2WI and SRT2WI through the Least Absolute Shrinkage and Selection Operator method. The performances of the models were evaluated by ROC, calibration, and decision curves. RESULTS ModelSRT2 outperformed modelHRT2 (AUC 0.869, sensitivity 71.1%, specificity 93.1%, and accuracy 83.3% vs. AUC 0.810, sensitivity 89.5%, specificity 70.1%, and accuracy 77.3%) in distinguishing T1/2 and T3/4 RC with significant difference (p < 0.05). Both radiomics models achieved higher AUCs than the expert radiologists (0.685, 95% confidence interval 0.595-0.775, p < 0.05). The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. CONCLUSIONS ModelSRT2 yielded superior predictive performance in preoperative RC T-staging by comparison with modelHRT2 and expert radiologists' visual assessments. KEY POINTS • For the first time, DL-based 3D SR images were applied in radiomics analysis for clinical utility. • Compared with the visual assessment of expert radiologists and the conventional radiomics model based on HRT2WI, the SR radiomics model showed a more favorable capability in helping clinicians assess the invasion depth of RC preoperatively. • This is the largest radiomics study for T-staging prediction in RC.
Collapse
|
24
|
CT-based radiomics to predict muscle invasion in bladder cancer. Eur Radiol 2022; 32:3260-3268. [DOI: 10.1007/s00330-021-08426-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 10/12/2021] [Accepted: 10/18/2021] [Indexed: 12/24/2022]
|
25
|
Refining neoadjuvant therapy clinical trial design for muscle-invasive bladder cancer before cystectomy: a joint US Food and Drug Administration and Bladder Cancer Advocacy Network workshop. Nat Rev Urol 2022; 19:37-46. [PMID: 34508246 DOI: 10.1038/s41585-021-00505-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 02/08/2023]
Abstract
The success of the use of novel therapies in the treatment of advanced urothelial carcinoma has contributed to growing interest in evaluating these therapies at earlier stages of the disease. However, trials evaluating these therapies in the neoadjuvant setting must have clearly defined study elements and appropriately selected end points to ensure the applicability of the trial and enable interpretation of the study results. To advance the development of rational trial design, a public workshop jointly sponsored by the US Food and Drug Administration and the Bladder Cancer Advocacy Network convened in August 2019. Clinicians, clinical trialists, radiologists, biostatisticians, patients, advocates and other stakeholders discussed key elements and end points when designing trials of neoadjuvant therapy for muscle-invasive bladder cancer (MIBC), identifying opportunities to refine eligibility, design and end points for neoadjuvant trials in MIBC. Although pathological complete response (pCR) is already being used as a co-primary end point, both individual-level and trial-level surrogacy for time-to-event end points, such as event-free survival or overall survival, remain incompletely characterized in MIBC. Additionally, use of pCR is limited by heterogeneity in pathological evaluation and the fact that the magnitude of pCR improvement that might translate into a meaningful clinical benefit remains unclear. Given existing knowledge gaps, capture of highly granular patient-related, tumour-related and treatment-related characteristics in the current generation of neoadjuvant MIBC trials will be critical to informing the design of future trials.
Collapse
|
26
|
Moitra D, Mandal RK. Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:10279-10297. [PMID: 35194379 PMCID: PMC8852869 DOI: 10.1007/s11042-022-12229-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/12/2021] [Accepted: 01/14/2022] [Indexed: 05/04/2023]
Abstract
Many significant efforts have so far been made to classify malignant tumors by using various machine learning methods. Most of the studies have considered a particular tumor genre categorized according to its originating organ. This has enriched the domain-specific knowledge of malignant tumor prediction, we are devoid of an efficient model that may predict the stages of tumors irrespective of their origin. Thus, there is ample opportunity to study if a heterogeneous collection of tumor images can be classified according to their respective stages. The present research work has prepared a heterogeneous tumor dataset comprising eight different datasets from The Cancer Imaging Archives and classified them according to their respective stages, as suggested by the American Joint Committee on Cancer. The proposed model has been used for classifying 717 subjects comprising different imaging modalities and varied Tumor-Node-Metastasis stages. A new non-sequential deep hybrid model ensemble has been developed by exploiting branched and re-injected layers, followed by bidirectional recurrent layers to classify tumor images. Results have been compared with standard sequential deep learning models and notable recent studies. The training and validation accuracy along with the ROC-AUC scores have been found satisfactory over the existing models. No model or method in the literature could ever classify such a diversified mix of tumor images with such high accuracy. The proposed model may help radiologists by acting as an auxiliary decision support system and speed up the tumor diagnosis process.
Collapse
|
27
|
Zheng Z, Gu Z, Xu F, Maskey N, He Y, Yan Y, Xu T, Liu S, Yao X. Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer. Cancer Imaging 2021; 21:65. [PMID: 34863282 PMCID: PMC8642943 DOI: 10.1186/s40644-021-00433-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.
Collapse
Affiliation(s)
- Zongtai Zheng
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Zhuoran Gu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Feijia Xu
- Department of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Niraj Maskey
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Yanyan He
- Department of Pathology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yang Yan
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Tianyuan Xu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Department of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shenghua Liu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
| |
Collapse
|
28
|
Gao RZ, Wen R, Wen DY, Huang J, Qin H, Li X, Wang XR, He Y, Yang H. Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:2685-2697. [PMID: 33615528 DOI: 10.1002/jum.15659] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 05/28/2023]
Abstract
OBJECTIVES To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. METHODS We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. RESULTS A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. CONCLUSIONS The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA.
Collapse
Affiliation(s)
- Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dong-Yue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jing Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| |
Collapse
|
29
|
Ye F, Hu Y, Gao J, Liang Y, Liu Y, Ou Y, Cheng Z, Jiang H. Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer. Front Immunol 2021; 12:722642. [PMID: 34733275 PMCID: PMC8559436 DOI: 10.3389/fimmu.2021.722642] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/30/2021] [Indexed: 12/12/2022] Open
Abstract
We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.
Collapse
Affiliation(s)
- Fangdie Ye
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yun Hu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiahao Gao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yingchun Liang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yufei Liu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxi Ou
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhang Cheng
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Haowen Jiang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
30
|
Wang W, Peng Y, Feng X, Zhao Y, Seeruttun SR, Zhang J, Cheng Z, Li Y, Liu Z, Zhou Z. Development and Validation of a Computed Tomography-Based Radiomics Signature to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Gastric Cancer. JAMA Netw Open 2021; 4:e2121143. [PMID: 34410397 PMCID: PMC8377567 DOI: 10.1001/jamanetworkopen.2021.21143] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
IMPORTANCE Neoadjuvant therapies have been shown to decrease tumor burden, increase resection rate, and improve the outcomes among patients with locally advanced gastric cancer (GC). However, not all patients are equally responsive; therefore, differentiating potential respondents from nonrespondents is clinically important. OBJECTIVE To use pretreatment computed tomography (CT)-pixelated feature-difference extraction techniques to identify diagnostically relevant features that could predict patients' response to neoadjuvant chemotherapy at diagnosis. DESIGN, SETTING, AND PARTICIPANTS This multicenter cohort study included patients with locally advanced GC who were treated from January 2010 to July 2017 at 2 hospitals in southern China (training cohort) and 1 hospital in northern China (external validation cohort). Their clinicopathological data, pretreatment CT images, and pathological reports were retrieved and analyzed. Data analysis was conducted from December 2017 to May 2021. EXPOSURES All patients underwent 2 to 4 cycles of fluorouracil in combination with a platinum-based neoadjuvant chemotherapy regimen. All gastrectomies were performed according to the Japanese Classification of Gastric Carcinoma (14th edition) guidelines. MAIN OUTCOMES AND MEASURES Reliability of clinicopathological and radiomics-based features were assessed with area under receiver operating characteristic curve (AUC) and Mann-Whitney U test. RESULTS A total of 323 patients (242 [74.9%] men; median [range] age, 58 [24-82] years) were included in the study, with 250 patients (77.4%) in the training cohort and 73 (22.6%) in the validation cohort. The baseline pretreatment characteristics of the training and validation cohorts were well-balanced. The number of respondents in the training and validation cohort was 122 (48.8%) and 40 (54.8%), respectively, and the number of nonrespondents was 128 (51.2%) and 33 (45.2%), respectively. No clinicopathological variables were significantly associated with treatment response. Using radiomics, 20 low-intercorrelated features from a total of 7477 features were used to construct a radiomics signature that demonstrated significant association with treatment response. Good discrimination performance of the radiomics signature for predicting treatment response in the training (AUC, 0.736; 95% CI, 0.675-0.798) and external validation (AUC, 0.679; 95% CI, 0.554-0.803) cohorts was observed. Decision curve analysis confirmed the clinical utility of the radiomics signature. CONCLUSIONS AND RELEVANCE In this study, the proposed radiomics signature showed potential as a clinical aid for predicting the response of patients with locally advanced GC before treatment, thereby allowing timely planning for effective treatments for potential nonrespondents.
Collapse
Affiliation(s)
- Wei Wang
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Ying Peng
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Xingyu Feng
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, People’s Republic of China
| | - Yan Zhao
- Department of Stomach Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital & Institute), Shenyang, People’s Republic of China
| | - Sharvesh Raj Seeruttun
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Jun Zhang
- Department of Stomach Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital & Institute), Shenyang, People’s Republic of China
| | - Zixuan Cheng
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, People’s Republic of China
| | - Yong Li
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, People’s Republic of China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, People’s Republic of China
| | - Zhiwei Zhou
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| |
Collapse
|
31
|
Xu X, Wang H, Guo Y, Zhang X, Li B, Du P, Liu Y, Lu H. Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer. Front Oncol 2021; 11:704039. [PMID: 34336691 PMCID: PMC8321511 DOI: 10.3389/fonc.2021.704039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/30/2021] [Indexed: 12/24/2022] Open
Abstract
Urinary bladder cancer (BCa) is a highly prevalent disease among aged males. Precise diagnosis of tumor phenotypes and recurrence risk is of vital importance in the clinical management of BCa. Although imaging modalities such as CT and multiparametric MRI have played an essential role in the noninvasive diagnosis and prognosis of BCa, radiomics has also shown great potential in the precise diagnosis of BCa and preoperative prediction of the recurrence risk. Radiomics-empowered image interpretation can amplify the differences in tumor heterogeneity between different phenotypes, i.e., high-grade vs. low-grade, early-stage vs. advanced-stage, and nonmuscle-invasive vs. muscle-invasive. With a multimodal radiomics strategy, the recurrence risk of BCa can be preoperatively predicted, providing critical information for the clinical decision making. We thus reviewed the rapid progress in the field of medical imaging empowered by the radiomics for decoding the phenotype and recurrence risk of BCa during the past 20 years, summarizing the entire pipeline of the radiomics strategy for the definition of BCa phenotype and recurrence risk including region of interest definition, radiomics feature extraction, tumor phenotype prediction and recurrence risk stratification. We particularly focus on current pitfalls, challenges and opportunities to promote massive clinical applications of radiomics pipeline in the near future.
Collapse
Affiliation(s)
- Xiaopan Xu
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Huanjun Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xi Zhang
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Peng Du
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Yang Liu
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Hongbing Lu
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| |
Collapse
|
32
|
Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int 2021; 100:870-880. [PMID: 34129883 DOI: 10.1016/j.kint.2021.05.031] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/15/2021] [Accepted: 05/14/2021] [Indexed: 02/06/2023]
Abstract
Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.
Collapse
Affiliation(s)
- Junjiong Zheng
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Hao Yu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jesur Batur
- Department of Urology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Zhenfeng Shi
- Department of Urology, the People's Hospital of Xinjiang Uyghur Autonomous Region, Xinjiang, People's Republic of China
| | - Aierken Tuerxun
- Department of Urology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Abudukeyoumu Abulajiang
- Department of Information Technology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Sihong Lu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Lifang Huang
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Ya Qiu
- Department of Radiology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Tianxin Lin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangdong, People's Republic of China.
| | - Xiaoguang Zou
- Department of Pharmacy, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China.
| |
Collapse
|
33
|
Zhang G, Wu Z, Xu L, Zhang X, Zhang D, Mao L, Li X, Xiao Y, Guo J, Ji Z, Sun H, Jin Z. Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer. Front Oncol 2021; 11:654685. [PMID: 34178641 PMCID: PMC8226179 DOI: 10.3389/fonc.2021.654685] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 05/27/2021] [Indexed: 12/12/2022] Open
Abstract
Background Clinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa. Methods A total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC) curves were performed and the area under the ROC curve (AUC) for discrimination between muscle-invasive BCa and non-muscle-invasive BCa was calculated. The performance of the model was evaluated and compared with that of the subjective assessment by two radiologists. Results The DL model exhibited relatively good performance in all cohorts [AUC: 0.861 in the internal validation cohort, 0.791 in the external validation cohort] and outperformed the two radiologists. The model yielded a sensitivity of 0.733, a specificity of 0.810 in the internal validation cohort and a sensitivity of 0.710 and a specificity of 0.773 in the external validation cohort. Conclusion The proposed DL model based on CT images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa.
Collapse
Affiliation(s)
- Gumuyang Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhe Wu
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, China
| | - Lili Xu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoxiao Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Daming Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Li Mao
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Xiuli Li
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Yu Xiao
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Guo
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, China
| | - Zhigang Ji
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Sun
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| |
Collapse
|
34
|
Kozikowski M, Suarez-Ibarrola R, Osiecki R, Bilski K, Gratzke C, Shariat SF, Miernik A, Dobruch J. Role of Radiomics in the Prediction of Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis. Eur Urol Focus 2021; 8:728-738. [PMID: 34099417 DOI: 10.1016/j.euf.2021.05.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 05/03/2021] [Accepted: 05/18/2021] [Indexed: 01/06/2023]
Abstract
CONTEXT Radiomics is a field of science that aims to develop improved methods of medical image analysis by extracting a large number of quantitative features. New data have emerged on the successful application of radiomics and machine-learning techniques to the prediction of muscle-invasive bladder cancer (MIBC). OBJECTIVE To systematically review the diagnostic performance of radiomic techniques in predicting MIBC. EVIDENCE ACQUISITION The literature search for relevant studies up to July 2020 was performed in the PubMed and EMBASE databases by two independent reviewers. The meta-analysis was inducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Inclusion criteria comprised studies that evaluated the diagnostic accuracy of radiomic models in predicting MIBC and used pathological examination as the reference standard. For bias assessment, Quality Assessment of Diagnostic Accuracy Studies-2 and Radiomic Quality Score were used. Weighted summary proportions were used to calculate pooled sensitivity and specificity. A linear mixed model was implemented to calculate the hierarchical summary receiver-operating characteristic (HSROC). Meta-regression analyses were performed to explore heterogeneity. EVIDENCE SYNTHESIS Eight studies with a total of 860 patients were included. The summary estimates for sensitivity and specificity in predicting MIBC were 82% (95% confidence interval [CI]: 77-86%) and 81% (95% CI: 76-85%), respectively. The area under HSROC was 0.88. There were no relevant heterogeneity in diagnostic accuracy measures (I2 = 33% and 41% for sensitivity and specificity, respectively), which was confirmed by a subsequent meta-regression analysis. CONCLUSIONS Radiomics shows high diagnostic performance in predicting MIBC. Despite differences in approaches, radiomic models were relatively homogeneous in their diagnostic accuracy. With further improvements, radiomics has the potential to become a useful adjunct in clinical management of bladder cancer. PATIENT SUMMARY Rapidly evolving imaging analysis methods using artificial intelligence algorithms, called radiomics, show high diagnostic performance in predicting muscle-invasive bladder cancer.
Collapse
Affiliation(s)
- Mieszko Kozikowski
- Urology Clinic, Centre of Postgraduate Medical Education, Department of Urology, Professor Witold Orlowski Independent Public Hospital, Warsaw, Poland.
| | - Rodrigo Suarez-Ibarrola
- Department of Urology, Faculty of Medicine, University of Freiburg Medical Centre, Freiburg, Germany
| | - Rafał Osiecki
- Urology Clinic, Centre of Postgraduate Medical Education, Department of Urology, Professor Witold Orlowski Independent Public Hospital, Warsaw, Poland
| | - Konrad Bilski
- Urology Clinic, Centre of Postgraduate Medical Education, Department of Urology, Professor Witold Orlowski Independent Public Hospital, Warsaw, Poland
| | - Christian Gratzke
- Department of Urology, Faculty of Medicine, University of Freiburg Medical Centre, Freiburg, Germany
| | - Shahrokh F Shariat
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Department of Urology, Weill Cornell Medical College, New York, NY, USA; Department of Urology, University of Texas Southwestern, Dallas, TX, USA; Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Arkadiusz Miernik
- Department of Urology, Faculty of Medicine, University of Freiburg Medical Centre, Freiburg, Germany
| | - Jakub Dobruch
- Urology Clinic, Centre of Postgraduate Medical Education, Department of Urology, Professor Witold Orlowski Independent Public Hospital, Warsaw, Poland
| |
Collapse
|
35
|
Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Eid M, Iannicelli E, Laghi A. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications. Cancers (Basel) 2021; 13:cancers13112681. [PMID: 34072366 PMCID: PMC8197789 DOI: 10.3390/cancers13112681] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine. Abstract Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.
Collapse
Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marwen Eid
- Internal Medicine, Northwell Health Staten Island University Hospital, Staten Island, New York, NY 10305, USA;
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-0633775285
| |
Collapse
|
36
|
Brodie A, Dai N, Teoh JYC, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021; 39:379-399. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/20/2020] [Accepted: 03/21/2021] [Indexed: 01/16/2023]
Abstract
There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.
Collapse
Affiliation(s)
- Andrew Brodie
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Nick Dai
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Prokar Dasgupta
- Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Nikhil Vasdev
- Hertfordshire and Bedfordshire Urological Cancer Centre, Department of Urology, Lister Hospital, Stevenage, United Kingdom; School of Medicine and Life Sciences, University of Hertfordshire, Hatfield, United Kingdom.
| |
Collapse
|
37
|
Zheng Z, Xu F, Gu Z, Yan Y, Xu T, Liu S, Yao X. Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer. Front Oncol 2021; 11:619893. [PMID: 34055600 PMCID: PMC8155615 DOI: 10.3389/fonc.2021.619893] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 04/26/2021] [Indexed: 12/31/2022] Open
Abstract
Background The treatment and prognosis for muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are different. We aimed to construct a nomogram based on the multiparametric MRI (mpMRI) radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score for the preoperative differentiation of MIBC from NMIBC. Method The retrospective study involved 185 pathologically confirmed bladder cancer (BCa) patients (training set: 129 patients, validation set: 56 patients) who received mpMRI before surgery between August 2014 to April 2020. A total of 2,436 radiomics features were quantitatively extracted from the largest lesion located on the axial T2WI and from dynamic contrast-enhancement images. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature screening. The selected features were introduced to construct radiomics signatures using three classifiers, including least absolute shrinkage and selection operator (LASSO), support vector machines (SVM) and random forest (RF) in the training set. The differentiation performances of the three classifiers were evaluated using the area under the curve (AUC) and accuracy. Univariable and multivariable logistic regression were used to develop a nomogram based on the optimal radiomics signature and clinical characteristics. The performance of the radiomics signatures and the nomogram was assessed and validated in the validation set. Results Compared to the RF and SVM classifiers, the LASSO classifier had the best capacity for muscle invasive status differentiation in both the training (accuracy: 90.7%, AUC: 0.934) and validation sets (accuracy: 87.5%, AUC: 0.906). Incorporating the radiomics signature and VI-RADS score, the nomogram demonstrated better discrimination and calibration both in the training set (accuracy: 93.0%, AUC: 0.970) and validation set (accuracy: 89.3%, AUC: 0.943). Decision curve analysis showed the clinical usefulness of the nomogram. Conclusions The mpMRI radiomics signature may be useful for the preoperative differentiation of muscle-invasive status in BCa. The proposed nomogram integrating the radiomics signature with the VI-RADS score may further increase the differentiation power and improve clinical decision making.
Collapse
Affiliation(s)
- Zongtai Zheng
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Feijia Xu
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhuoran Gu
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yang Yan
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tianyuan Xu
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.,Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shenghua Liu
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| |
Collapse
|
38
|
Abstract
Cystoscopic examination remains the gold standard technique for initial diagnosis of bladder cancer (BCa). Despite significant progress in enhanced cystoscopic techniques, blue light cystoscopy and narrow band imaging are the only ones well supported by high-level evidence and, if available, should be used during initial staging of BCa. Multiparametric MRI could be an important imaging tool in local staging of BCa. With ever-expanding targeted therapy and immunotherapy options in both muscle-invasive and non-muscle-invasive BCa, molecular subtyping could become an essential part of initial histologic staging in the near future.
Collapse
Affiliation(s)
- Hamed Ahmadi
- Department of Urology, University of Southern California/Norris Comprehensive Cancer Center, 1441 Eastlake Avenue, Suite 7416, Los Angeles, CA 90089, USA
| | - Vinay Duddalwar
- Department of Radiology, University of Southern California, 1441 Eastlake Avenue, Los Angeles, CA 90089, USA
| | - Siamak Daneshmand
- Department of Urology, University of Southern California/Norris Comprehensive Cancer Center, 1441 Eastlake Avenue, Suite 7416, Los Angeles, CA 90089, USA; USC/Norris Comprehensive Cancer Center, USC Institute of Urology, 1441 Eastlake Avenue, Suite 7416, Los Angeles, CA 90089, USA.
| |
Collapse
|
39
|
Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram. Eur Radiol 2021; 31:7855-7864. [PMID: 33864139 DOI: 10.1007/s00330-021-07902-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 02/13/2021] [Accepted: 03/16/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVES To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). METHODS In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. RESULTS In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). CONCLUSIONS The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.
Collapse
|
40
|
Yang Y, Zou X, Wang Y, Ma X. Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non-muscle-invasive bladder cancer with CT. Eur J Radiol 2021; 139:109666. [PMID: 33798819 DOI: 10.1016/j.ejrad.2021.109666] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 02/22/2021] [Accepted: 03/13/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To construct a deep-learning convolution neural network (DL-CNN) system for the differentiation of muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) on contrast-enhanced computed tomography (CT) images in patients with bladder cancer. MATERIALS AND METHODS A total of 1200 cross-sectional CT images were obtained from 369 patients with bladder cancer receiving radical cystectomy from January 2015 to June 2018, including 249 non-muscle-invasive bladder cancer (NMIBC) series and 120 muscle-invasive bladder cancer (MIBC) series. All eligible images were distributed randomly into the training, validation, and testing cohorts with ratios of 70 %, 15 %, and 15 %, respectively. We developed one small DL-CNN containing four convolutional and max pooling layers and eight DL-CNNs with pretrained bases from the ImageNet dataset to differentiate NMIBC from MIBC. The intermediate activations were applied on the test dataset to visualize how successive DL-CNN layers transform their input. RESULTS The area under the receiver operating characteristic curve (AUROC) of the validation and testing datasets for the small DL-CNN was 0.946 and 0.998, respectively. The AUROCs of eight deep learning algorithms with pretrained bases ranged from 0.762 to 0.997 in the testing dataset. The VGG16 model had the largest AUROC of 0.997 among the eight algorithms with a sensitivity and specificity of 0.889 and 0.989. The independent features encoded by the small DL-CNN filters were displayed as assemblies of individual channels. CONCLUSION Based on contrast-enhanced CT images, our DL-CNN system could successfully classify NMIBC and MIBC with favorable AUROC in patients with bladder cancer. The application of our system in early stage might assist the pathological examination for the improvement of diagnostic accuracy.
Collapse
Affiliation(s)
- Yuhan Yang
- West China School of Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, Sichuan, China.
| | - Xiuhe Zou
- West China Hospital, Sichuan University, Guoxue Road 37, Chengdu, 610041, China.
| | - Yixi Wang
- West China School of Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, Sichuan, China.
| | - Xuelei Ma
- Department of Biotherapy and Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu, 610041, China.
| |
Collapse
|
41
|
Ma G, Kang J, Qiao N, Zhang B, Chen X, Li G, Gao Z, Gui S. Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery. Front Oncol 2021; 10:599888. [PMID: 33680925 PMCID: PMC7925821 DOI: 10.3389/fonc.2020.599888] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/30/2020] [Indexed: 01/06/2023] Open
Abstract
Purpose Craniopharyngiomas (CPs) are benign tumors, complete tumor resection is considered to be the optimal treatment. However, although histologically benign, the local invasiveness of CPs commonly contributes to incomplete resection and a poor prognosis. At present, some advocate less aggressive surgery combined with radiotherapy as a more reasonable and effective means of protecting hypothalamus function and preventing recurrence in patients with tight tumor adhesion to the hypothalamus. Hence, if a method can be developed to predict the invasiveness of CP preoperatively, it will help in the development of a more personalized surgical strategy. The aim of the study was to report a radiomics-clinical nomogram for the individualized preoperative prediction of the invasiveness of adamantinomatous CP (ACPs) before surgery. Methods In total, 1,874 radiomics features were extracted from whole tumors on contrast-enhanced T1-weighted images. A support vector machine trained a predictive model that was validated using receiver operating characteristic (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction. Results Eleven features associated with the invasiveness of ACPs were selected by using the least absolute shrinkage and selection operator (LASSO) method. These features yielded area under the curve (AUC) values of 79.09 and 73.5% for the training and test sets, respectively. The nomogram incorporating peritumoral edema and the radiomics signature yielded good calibration in the training and test sets with the AUCs of 84.79 and 76.48%, respectively. Conclusion The developed model yields good performance, indicating that the invasiveness of APCs can be predicted using noninvasive radiological data. This reliable, noninvasive tool can help clinical decision making and improve patient prognosis.
Collapse
Affiliation(s)
- Guofo Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jie Kang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ning Qiao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bochao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuzhu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guilin Li
- Neuropathology Department, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhixian Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songbai Gui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
42
|
Zhou Y, Ma XL, Zhang T, Wang J, Zhang T, Tian R. Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging 2021; 48:2904-2913. [PMID: 33547553 DOI: 10.1007/s00259-021-05220-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE This study was designed and performed to assess the ability of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomics features combined with machine learning methods to differentiate between primary and metastatic lung lesions and to classify histological subtypes. Moreover, we identified the optimal machine learning method. METHODS A total of 769 patients pathologically diagnosed with primary or metastatic lung cancers were enrolled. We used the LIFEx package to extract radiological features from semiautomatically segmented PET and CT images within the same volume of interest. Patients were randomly distributed in training and validation sets. Through the evaluation of five feature selection methods and nine classification methods, discriminant models were established. The robustness of the procedure was controlled by tenfold cross-validation. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS Based on the radiomics features extracted from PET and CT images, forty-five discriminative models were established. Combined with appropriate feature selection methods, most classifiers showed excellent discriminative ability with AUCs greater than 0.75. In the differentiation between primary and metastatic lung lesions, the feature selection method gradient boosting decision tree (GBDT) combined with the classifier GBDT achieved the highest classification AUC of 0.983 in the PET dataset. In contrast, the feature selection method eXtreme gradient boosting combined with the classifier random forest (RF) achieved the highest AUC of 0.828 in the CT dataset. In the discrimination between squamous cell carcinoma and adenocarcinoma, the combination of GBDT feature selection method with GBDT classification had the highest AUC of 0.897 in the PET dataset. In contrast, the combination of the GBDT feature selection method with the RF classification had the highest AUC of 0.839 in the CT dataset. Most of the decision tree (DT)-based models were overfitted, suggesting that the classification method was not appropriate for practical application. CONCLUSION 18F-FDG PET/CT radiomics features combined with machine learning methods can distinguish between primary and metastatic lung lesions and identify histological subtypes in lung cancer. GBDT and RF were considered optimal classification methods for the PET and CT datasets, respectively, and GBDT was considered the optimal feature selection method in our analysis.
Collapse
Affiliation(s)
- Yi Zhou
- Department of Nuclear Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Xue-Lei Ma
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Ting Zhang
- West China School of Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Jian Wang
- School of Computer Science, Nanjing University of Science and Technology, No. 200, Xiaolinwei Road, Nanjing, 210094, China
| | - Tao Zhang
- West China School of Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China.
| |
Collapse
|
43
|
Zheng Z, Chen Z, Xie Y, Zhong Q, Xie W. Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades. Eur Radiol 2021; 31:6078-6086. [PMID: 33515086 DOI: 10.1007/s00330-020-07667-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/12/2020] [Accepted: 12/22/2020] [Indexed: 12/29/2022]
Abstract
OBJECTIVES Nuclear grades are proved to be one of the most significant prognostic factors for clear cell renal cell carcinoma (ccRCC). Radiomics nomogram is a widely used noninvasive tool that could predict tumor phenotypes. In this study, we performed radiomics analysis to develop and validate a CT-based nomogram for the preoperative prediction of nuclear grades in ccRCC. METHOD CT images and clinical data of 258 ccRCC patients were retrieved from the Cancer Imaging Archive (TCIA). Radiomics features were extracted from arterial-phase CT images using 3D Slicer software. LASSO regression model was performed to develop a radiomics signature in the training set (n = 143). A radiomics nomogram was constructed combining radiomics signature and selected clinical predictors. Receiver operating characteristic (ROC) curve and calibration curve were used to determine the performance of the radiomics nomogram in the training and validation set (n = 115). Decision curve analysis was used to assess the clinical usefulness of the CT-based nomogram. RESULTS One thousand three hundred sixteen radiomics features were extracted from arterial-phase CT images. A radiomics signature, consisting of 20 features, was developed and showed a favorable performance in discriminating nuclear grades with an area under the curve (AUC) of 0.914 and 0.846 in the training and validation set, respectively. The CT-based nomogram, including the radiomics signature and the CT-determined T stage, achieved good calibration and discrimination in the training set (AUC, 0.929; 95% CI, 0.886-0.972) and validation set (AUC, 0.876; 95% CI, 0.812-0.939). Decision curve analysis demonstrated the clinical usefulness of the CT-based nomogram. CONCLUSION The noninvasive CT-based nomogram, including radiomics signature and CT-determined T stage, could improve the accuracy of preoperative grading of ccRCC and provide individualized treatment for ccRCC patients. KEY POINTS • Contrast-enhanced CT may help in preoperative grading of ccRCC. • The CT-based nomogram incorporated a radiomics signature and CT-determined T stage could preoperatively predict ccRCC grades. • The CT-based nomogram has the potential to improve individualized treatment and assist clinical decision making of ccRCC patients.
Collapse
Affiliation(s)
- Zaosong Zheng
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zhiliang Chen
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yingwei Xie
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Qiyu Zhong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wenlian Xie
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
| |
Collapse
|
44
|
Dana J, Agnus V, Ouhmich F, Gallix B. Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective. Semin Nucl Med 2020; 50:541-548. [PMID: 33059823 DOI: 10.1053/j.semnuclmed.2020.07.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Research in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) have become widespread clinical tools. This may be due to our inability to accurately characterize tumors and monitor intratumoral changes in imaging. Artificial intelligence, through machine learning and deep learning, opens a new path in medical research because it can bring together a large amount of heterogeneous data into the same analysis to reach a single outcome. Supervised or unsupervised learning may lead to new paradigms by identifying unrevealed structural patterns across data. Deep learning will provide human-free, undefined upstream, reproducible, and automated quantitative imaging biomarkers. Since tumor phenotype is driven by its genotype and thus indirectly defines tumoral progression, tumor characterization using machine learning and deep learning algorithms will allow us to monitor molecular expression noninvasively, anticipate therapeutic failure, and lead therapeutic management. To follow this path, quality standards have to be set: standardization of imaging acquisition as it has been done in the field of biology, transparency of the model development as it should be reproducible by different institutions, validation, and testing through a high-quality process using large and complex open databases and better interpretability of these algorithms.
Collapse
Affiliation(s)
- Jérémy Dana
- IHU of Strasbourg, Strasbourg, France; Inserm & University of Strasbourg UMR-S1110, Strasbourg, France; Faculty of Medicine, University of Paris, Paris, France
| | - Vincent Agnus
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Farid Ouhmich
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Benoit Gallix
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France; Faculty of Medicine, University of Strasbourg, Strasbourg, France; Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
45
|
Cacciamani GE, Nassiri N, Varghese B, Maas M, King KG, Hwang D, Abreu A, Gill I, Duddalwar V. Radiomics and Bladder Cancer: Current Status. Bladder Cancer 2020. [DOI: 10.3233/blc-200293] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE: To systematically review the current literature and discuss the applications and limitations of radiomics and machine-learning augmented radiomics in the management of bladder cancer. METHODS: Pubmed ®, Scopus ®, and Web of Science ® databases were searched systematically for all full-text English-language articles assessing the impact of Artificial Intelligence OR Radiomics OR Machine Learning AND Bladder Cancer AND (staging OR grading OR prognosis) published up to January 2020. RESULTS: Of the 686 articles that were identified, 13 studies met the criteria for quantitative analysis. Staging, Grading and Tumor Classification, Prognosis, and Therapy Response were discussed in 7, 3, 2 and 7 studies, respectively. Data on cost of implementation were not reported. CT and MRI were the most common imaging approaches. CONCLUSION: Radiomics shows potential in bladder cancer detection, staging, grading, and response to therapy, thereby supporting the physician in personalizing patient management. Extension and validation of this promising technology in large multisite prospective trials is warranted to pave the way for its clinical translation.
Collapse
Affiliation(s)
- Giovanni E. Cacciamani
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Norris Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Nima Nassiri
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bino Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marissa Maas
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kevin G. King
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darryl Hwang
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andre Abreu
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Inderbir Gill
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Norris Cancer Center, University of Southern California, Los Angeles, CA, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
46
|
Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas. Eur J Radiol 2020; 132:109287. [PMID: 32980725 DOI: 10.1016/j.ejrad.2020.109287] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 09/11/2020] [Accepted: 09/13/2020] [Indexed: 01/11/2023]
Abstract
PURPOSE Bone invasion in meningiomas is a prognostic determinant, and a priori knowledge may alter surgical techniques. Here, we aim to predict bone invasion in meningiomas using radiomic signatures based on preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging (MRI). METHODS In this retrospective study, 490 patients diagnosed with meningiomas, including WHO grade I (448cases), grade II (38cases), and grade III (4cases), were enrolled and 213 out of 490 cases (43.5 %) had bone invasion. The patients were randomly divided into training (n = 343) and test (n = 147) datasets at a 7:3 ratio. For each patient, 1227 radiomic features were extracted from T1C and T2, respectively. Spearman's correlation and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select the most informative features. Subsequently, a 5-fold cross-validation was used to compare the performance of different classification algorithms, and logistic regression was chosen to predict the risk of bone invasion. RESULTS Eight radiomic features were selected from T1C and T2 respectively, and three models were built using radiomic features. The radiomic models derived from T1C alone or a combination of T1C and T2 had the best performance in predicting risk of bone invasion, with areas under the curve in the training dataset of 0.714 [95 % CI, 0.660-0.768] and 0.722 [95 % CI, 0.668-0.776] and in the test datasets of 0.715 [95 % CI, 0.632-0.798] and 0.713 [95 % CI, 0.628-0.798], respectively. CONCLUSIONS The radiomic model may aid clinicians with preoperative prediction of bone invasion by meningiomas, which can help in predicting prognosis and devising surgical strategies.
Collapse
|
47
|
Diao X, Cai J, Zheng J, Kong J, Wu S, Yu H, Huang H, Xie W, Chen X, Huang C, Huang L, Qin H, Huang J, Lin T. Association of chromosome 7 aneuploidy measured by fluorescence in situ hybridization assay with muscular invasion in bladder cancer. Cancer Commun (Lond) 2020; 40:167-180. [PMID: 32279463 PMCID: PMC7170658 DOI: 10.1002/cac2.12017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 02/27/2020] [Accepted: 03/02/2020] [Indexed: 12/29/2022] Open
Abstract
Background The preoperative prediction of muscular invasion status is important for adequately treating bladder cancer (BC) but nevertheless, there are some existing dilemmas in the current preoperative diagnostic accuracy of BC with muscular invasion. Here, we investigated the potential association between the fluorescence in situ hybridization (FISH) assay and muscular invasion among patients with BC. A cytogenetic‐clinical nomogram for the individualized preoperative differentiation of muscle‐invasive BC (MIBC) from non‐muscle‐invasive BC (NMIBC) is also proposed. Methods All eligible BC patients were preoperatively tested using a FISH assay, which included 4 sites (chromosome‐specific centromeric probe [CSP] 3, 7, and 17, and gene locus‐specific probe [GLP]‐p16 locus). The correlation between the FISH assay and BC muscular invasion was evaluated using the Chi‐square tests. In the training set, univariate and multivariate logistic regression analyses were used to develop a cytogenetic‐clinical nomogram for preoperative muscular invasion prediction. Then, we assessed the performance of the nomogram in the training set with respect to its discriminatory accuracy and calibration for predicting muscular invasion, and clinical usefulness, which were then validated in the validation set. Moreover, model comparison was set to evaluate the discrimination and clinical usefulness between the nomogram and the individual variables incorporated in the nomogram. Results Muscular invasion was more prevalent in BC patients with positive CSP3, CSP7 and CSP17 status (OR [95% CI], 2.724 [1.555 to 4.774], P < 0.001; 3.406 [1.912 to 6.068], P < 0.001 and 2.483 [1.436 to 4.292], P = 0.001, respectively). Radiology‐determined tumor size, radiology‐determined clinical tumor stage and CSP7 status were identified as independent risk factors of BC muscular invasion by the multivariate regression analysis in the training set. Then, a cytogenetic‐clinical nomogram incorporating these three independent risk factors was constructed and was observed to have satisfactory discrimination in the training (AUC 0.784; 95% CI: 0.715 to 0.853) and validation (AUC 0.743; 95% CI: 0.635 to 0.850) set. The decision curve analysis (DCA) indicated the clinical usefulness of our nomogram. In models comparison, using the receiver operator characteristic (ROC) analyses, the nomogram showed higher discriminatory accuracy than any variables incorporated in the nomogram alone and the DCAs also identified the nomogram as possessing the highest net benefits at wide range of threshold probabilities. Conclusion CSP7 status was identified as an independent factor for predicting muscular invasion in BC patients and was successfully incorporated in a clinical nomogram combining the results of the FISH assay with clinical risk factors.
Collapse
Affiliation(s)
- Xiayao Diao
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Jinhua Cai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Junjiong Zheng
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Hao Yu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Hao Huang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Weibin Xie
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Xiong Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Chengran Huang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China
| | - Lifang Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China.,State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, 510120, P. R. China
| | - Haide Qin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China.,State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, 510120, P. R. China
| | - Jian Huang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China.,State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, 510120, P. R. China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, P. R. China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510120, P. R. China.,State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, 510120, P. R. China
| |
Collapse
|
48
|
Tang X, Xu X, Han Z, Bai G, Wang H, Liu Y, Du P, Liang Z, Zhang J, Lu H, Yin H. Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer. Biomed Eng Online 2020; 19:5. [PMID: 31964407 PMCID: PMC6975040 DOI: 10.1186/s12938-019-0744-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 12/27/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Non-invasive discrimination between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes of non-small-cell lung cancer (NSCLC) could be very beneficial to the patients unfit for the invasive diagnostic procedures. The aim of this study was to investigate the feasibility of utilizing the multimodal magnetic resonance imaging (MRI) radiomics and clinical features in classifying NSCLC. This retrospective study involved 148 eligible patients with postoperative pathologically confirmed NSCLC. The study was conducted in three steps: (1) feature extraction was performed using the online freely available package with the multimodal MRI data; (2) feature selection was performed using the Student's t test and support vector machine (SVM)-based recursive feature elimination method with the training cohort (n = 100), and the performance of these selected features was evaluated using both the training and the validation cohorts (n = 48) with a non-linear SVM classifier; (3) a Radscore model was then generated using logistic regression algorithm; (4) Integrating the Radscore with the semantic clinical features, a radiomics-clinical nomogram was developed, and its overall performance was evaluated with both cohorts. RESULTS Thirteen optimal features achieved favorable discrimination performance with both cohorts, with area under the curve (AUC) of 0.819 and 0.824, respectively. The radiomics-clinical nomogram integrating the Radscore with the independent clinical predictors exhibited more favorable discriminative power, with AUC improved to 0.901 and 0.872 in both cohorts, respectively. The Hosmer-Lemeshow test and decision curve analysis results furtherly showed good predictive precision and clinical usefulness of the nomogram. CONCLUSION Non-invasive histological subtype stratification of NSCLC can be done favorably using multimodal MRI radiomics features. Integrating the radiomics features with the clinical features could further improve the performance of the histological subtype stratification in patients with NSCLC.
Collapse
Affiliation(s)
- Xing Tang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China
| | - Xiaopan Xu
- School of Biomedical Engineering, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China
| | - Zhiping Han
- Department of Respiratory Medicine, Xijing Hospital, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China
| | - Guoyan Bai
- Department of Clinical Laboratory, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, People's Republic of China
| | - Hong Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China
| | - Yang Liu
- School of Biomedical Engineering, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China
| | - Peng Du
- School of Biomedical Engineering, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China
| | - Zhengrong Liang
- Departments of Radiology, School of Computer Science and Biomedical Engineering, State University of New York, Stony Brook, NY, USA
| | - Jian Zhang
- Department of Respiratory Medicine, Xijing Hospital, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China.
| | - Hongbing Lu
- School of Biomedical Engineering, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China.
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 169 Changle West Road, Xi'an, 710032, Shaanxi, People's Republic of China.
| |
Collapse
|
49
|
Ge L, Chen Y, Yan C, Zhao P, Zhang P, A R, Liu J. Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management. Front Oncol 2019; 9:1296. [PMID: 31850202 PMCID: PMC6892826 DOI: 10.3389/fonc.2019.01296] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 11/08/2019] [Indexed: 02/05/2023] Open
Abstract
Bladder cancer is a fatal cancer that happens in the genitourinary tract with quite high morbidity and mortality annually. The high level of recurrence rate ranging from 50 to 80% makes bladder cancer one of the most challenging and costly diseases to manage. Faced with various problems in existing methods, a recently emerging concept for the measurement of imaging biomarkers and extraction of quantitative features called "radiomics" shows great potential in the application of detection, grading, and follow-up management of bladder cancer. Furthermore, machine-learning (ML) algorithms on the basis of "big data" are fueling the powers of radiomics for bladder cancer monitoring in the era of precision medicine. Currently, the usefulness of the novel combination of radiomics and ML has been demonstrated by a large number of successful cases. It possesses outstanding strengths including non-invasiveness, low cost, and high efficiency, which may serve as a revolution to tumor assessment and emancipate workforce. However, for the extensive clinical application in the future, more efforts should be made to break down the limitations caused by technology deficiencies, inherent problems during the process of radiomic analysis, as well as the quality of present studies.
Collapse
Affiliation(s)
- Lingling Ge
- West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Radiological Department, West China Hospital, Sichuan University, Chengdu, China
| | - Chunyi Yan
- West China Hospital, Sichuan University, Chengdu, China
| | - Pan Zhao
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Peng Zhang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Runa A
- Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University, Chengdu, China
| | - Jiaming Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
50
|
Nomograms for predicting long-term overall survival and cancer-specific survival in patients with primary urethral carcinoma: a population-based study. Int Urol Nephrol 2019; 52:287-300. [PMID: 31612421 DOI: 10.1007/s11255-019-02314-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 10/04/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND Our aim was to identify the independent prognostic factors in patients with primary urethral carcinoma (PUC) and to predict their overall survival (OS) and cancer-specific survival (CSS) at 3, 5, and 8 years. METHODS Patients with PUC identified in the Surveillance, Epidemiology, and End Results (SEER) database were divided into training and validation cohorts. Nomograms were constructed based on the results of Cox regression analysis. The predictive performance of each nomogram was evaluated using the consistency index (C-index), the area under the receiver operating characteristics curve (AUC), and calibration plots. Decision-curve analysis (DCA) was used to test the clinical value of the predictive models. RESULTS Our study screened 822 patients with PUC. Multivariate analysis showed that the age at diagnosis, race, histology, American Joint Committee on Cancer (AJCC) stage, and surgery status were independent prognostic factors for CSS and age at diagnosis, race, histology, AJCC stage, surgery status, and chemotherapy for OS (all P < 0.05). We used these prognostic factors to construct nomograms. The C-indexes for OS and CSS were 0.713 and 0.741 in training cohorts and 0.714 and 0.738 in validation cohorts, respectively. The AUC and calibration plots demonstrated the good performance of both nomograms. The DCA indicated the presence of clinical net benefits in both the training and validation cohorts. CONCLUSION We developed and validated nomograms for predicting OS and CSS in patients with PUC, which can help clinicians make treatment decisions.
Collapse
|