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Yang G, Bai J, Hao M, Zhang L, Fan Z, Wang X. Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics. Insights Imaging 2024; 15:88. [PMID: 38526620 DOI: 10.1186/s13244-024-01662-3] [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: 11/02/2023] [Accepted: 03/04/2024] [Indexed: 03/27/2024] Open
Abstract
OBJECTIVE We aimed to develop a radiomics-clinical nomogram using multi-sequence MRI to predict recurrence-free survival (RFS) in bladder cancer (BCa) patients and assess its superiority over clinical models. METHODS A retrospective cohort of 229 BCa patients with preoperative multi-sequence MRI was divided into a training set (n = 160) and a validation set (n = 69). Radiomics features were extracted from T2-weighted images, diffusion-weighted imaging, apparent diffusion coefficient, and dynamic contrast-enhanced images. Effective features were identified using the least absolute shrinkage and selection operator (LASSO) method. Clinical risk factors were determined via univariate and multivariate Cox analysis, leading to the creation of a radiomics-clinical nomogram. Kaplan-Meier analysis and log-rank tests assessed the relationship between radiomics features and RFS. We calculated the net reclassification improvement (NRI) to evaluate the added value of the radiomics signature and used decision curve analysis (DCA) to assess the nomogram's clinical validity. RESULTS Radiomics features significantly correlated with RFS (log-rank p < 0.001) and were independent of clinical factors (p < 0.001). The combined model, incorporating radiomics features and clinical data, demonstrated the best prognostic value, with C-index values of 0.853 in the training set and 0.832 in the validation set. Compared to the clinical model, the radiomics-clinical nomogram exhibited superior calibration and classification (NRI: 0.6768, 95% CI: 0.5549-0.7987, p < 0.001). CONCLUSION The radiomics-clinical nomogram, based on multi-sequence MRI, effectively assesses the BCa recurrence risk. It outperforms both the radiomics model and the clinical model in predicting BCa recurrence risk. CRITICAL RELEVANCE STATEMENT The radiomics-clinical nomogram, utilizing multi-sequence MRI, holds promise for predicting bladder cancer recurrence, enhancing individualized clinical treatment, and performing tumor surveillance. KEY POINTS • Radiomics plays a vital role in predicting bladder cancer recurrence. • Precise prediction of tumor recurrence risk is crucial for clinical management. • MRI-based radiomics models excel in predicting bladder cancer recurrence.
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Affiliation(s)
- Guoqiang Yang
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jingjing Bai
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Min Hao
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Lu Zhang
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zhichang Fan
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaochun Wang
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
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Chen G, Fan X, Wang T, Zhang E, Shao J, Chen S, Zhang D, Zhang J, Guo T, Yuan Z, Tang H, Yu Y, Chen J, Wang X. A machine learning model based on MRI for the preoperative prediction of bladder cancer invasion depth. Eur Radiol 2023; 33:8821-8832. [PMID: 37470826 DOI: 10.1007/s00330-023-09960-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 04/27/2023] [Accepted: 05/24/2023] [Indexed: 07/21/2023]
Abstract
OBJECTIVES To construct and validate a prediction model based on full-sequence MRI for preoperatively evaluating the invasion depth of bladder cancer. METHODS A total of 445 patients with bladder cancer were divided into a seven-to-three training set and test set for each group. The radiomic features of lesions were extracted automatically from the preoperative MRI images. Two feature selection methods were performed and compared, the key of which are the Least Absolute Shrinkage and Selection Operator (LASSO) and the Max Relevance Min Redundancy (mRMR). The classifier of the prediction model was selected from six advanced machine-learning techniques. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were applied to assess the efficiency of the models. RESULTS The models with the best performance for pathological invasion prediction and muscular invasion prediction consisted of LASSO as the feature selection method and random forest as the classifier. In the training set, the AUC of the pathological invasion model and muscular invasion model were 0.808 and 0.828. Furthermore, with the mRMR as the feature selection method, the external invasion model based on random forest achieved excellent discrimination (AUC, 0.857). CONCLUSIONS The full-sequence models demonstrated excellent accuracy for preoperatively predicting the bladder cancer invasion status. CLINICAL RELEVANCE STATEMENT This study introduces a full-sequence MRI model for preoperative prediction of the depth of bladder cancer infiltration, which could help clinicians to recognise pathological features associated with tumour infiltration prior to invasive procedures. KEY POINTS • Full-sequence MRI prediction model performed better than Vesicle Imaging-Reporting and Data System (VI-RADS) for preoperatively evaluating the invasion status of bladder cancer. • Machine learning methods can extract information from T1-weighted image (T1WI) sequences and benefit bladder cancer invasion prediction.
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Affiliation(s)
- Guihua Chen
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Xuhui Fan
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Encheng Zhang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Jialiang Shao
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Siteng Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200135, China
| | - Dongliang Zhang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Jian Zhang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Tuanjie Guo
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Zhihao Yuan
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Heting Tang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Yaoyu Yu
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Jinyuan Chen
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| | - Xiang Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
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Ye Y, Luo Z, Qiu Z, Cao K, Huang B, Deng L, Zhang W, Liu G, Zou Y, Zhang J, Li J. Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation. Bioengineering (Basel) 2023; 10:1355. [PMID: 38135946 PMCID: PMC10740947 DOI: 10.3390/bioengineering10121355] [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: 09/22/2023] [Revised: 11/10/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.
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Affiliation(s)
- Yaojiang Ye
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
| | - Zixin Luo
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Zhengxuan Qiu
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; (Z.L.); (Z.Q.); (K.C.); (B.H.)
| | - Lei Deng
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
| | - Weijing Zhang
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China;
| | - Guoqing Liu
- College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Yujian Zou
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
| | - Jian Zhang
- Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518060, China
| | - Jianpeng Li
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan 523059, China; (Y.Y.); (L.D.); (Y.Z.)
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Yilmaz EC, Belue MJ, Turkbey B, Reinhold C, Choyke PL. A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging. Can Assoc Radiol J 2023; 74:534-547. [PMID: 36515576 DOI: 10.1177/08465371221135782] [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] [Indexed: 12/15/2022] Open
Abstract
Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.
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Affiliation(s)
- Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Caroline Reinhold
- McGill University Health Center, McGill University, Montreal, Canada
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
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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.
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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
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Qin Y, Hu J, Han J. A 2OURSR: Adaptive adjustment based real MRI super-resolution via opinion-unaware measurements. Comput Med Imaging Graph 2023; 107:102247. [PMID: 37224741 DOI: 10.1016/j.compmedimag.2023.102247] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 05/26/2023]
Abstract
High-quality and high-resolution magnetic resonance (MR) images can provide more details for diagnosis and analyses. Recently, MR images guided neurosurgery has become an emerging technique in clinics. Unlike other medical imaging techniques, it is impossible to achieve both real-time imaging and high image quality in MR imaging. The real-time performance is closely related to the nuclear magnetic equipment itself as well as the collection strategy of the k space data. Optimizing the imaging time cost via the corresponding algorithm is harder than enhancing image quality. Further, in reconstructing low-resolution and noise-rich MR images, getting relatively high-definition and resolution MR images as references are difficult or impossible. In addition, the existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. As a result, severely bad results are inevitable when the modeling assumptions are far apart from the actual situation. To address these problems, we propose a novel adaptive adjustment method based on real MR images via opinion-unaware measurements for real super-resolution (A2OURSR). It can estimate the degree of blur and noise from the test image itself using two scores. These two scores can be considered pseudo labels to train the adaptive adjustable degradation estimation module. Then, the outputs of the above model are used as the inputs of the conditional network to tweak the generated results. Thus, the results can be automatically adjusted via the whole dynamic model. Extensive experimental results show that the proposed A2OURSR is superior to state-of-the-art methods on benchmarks quantitatively and visually.
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Affiliation(s)
- Yanding Qin
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China
| | - Jinbin Hu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
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Li J, Qiu Z, Cao K, Deng L, Zhang W, Xie C, Yang S, Yue P, Zhong J, Lyu J, Huang X, Zhang K, Zou Y, Huang B. Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107466. [PMID: 36907040 DOI: 10.1016/j.cmpb.2023.107466] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/09/2023] [Accepted: 03/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI). METHODS A total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models. RESULTS The area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model. CONCLUSIONS The T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.
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Affiliation(s)
- Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Zhengxuan Qiu
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Lei Deng
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Weijing Zhang
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuanmiao Xie
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shuiqing Yang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Peiyan Yue
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Jian Zhong
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Jiegeng Lyu
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Xiang Huang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Kunlin Zhang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Yujian Zou
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China.
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
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8
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Sarkar S, Min K, Ikram W, Tatton RW, Riaz IB, Silva AC, Bryce AH, Moore C, Ho TH, Sonpavde G, Abdul-Muhsin HM, Singh P, Wu T. Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach. Cancers (Basel) 2023; 15:cancers15061673. [PMID: 36980557 PMCID: PMC10046500 DOI: 10.3390/cancers15061673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023] Open
Abstract
Accurate clinical staging of bladder cancer aids in optimizing the process of clinical decision-making, thereby tailoring the effective treatment and management of patients. While several radiomics approaches have been developed to facilitate the process of clinical diagnosis and staging of bladder cancer using grayscale computed tomography (CT) scans, the performances of these models have been low, with little validation and no clear consensus on specific imaging signatures. We propose a hybrid framework comprising pre-trained deep neural networks for feature extraction, in combination with statistical machine learning techniques for classification, which is capable of performing the following classification tasks: (1) bladder cancer tissue vs. normal tissue, (2) muscle-invasive bladder cancer (MIBC) vs. non-muscle-invasive bladder cancer (NMIBC), and (3) post-treatment changes (PTC) vs. MIBC.
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Affiliation(s)
- Suryadipto Sarkar
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
- Correspondence:
| | - Kong Min
- Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Waleed Ikram
- Division of Hematology and Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Ryan W. Tatton
- Division of Hematology and Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Irbaz B. Riaz
- Division of Hematology and Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Alvin C. Silva
- Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Alan H. Bryce
- Division of Hematology and Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Cassandra Moore
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Thai H. Ho
- Division of Hematology and Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Guru Sonpavde
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | | | - Parminder Singh
- Division of Hematology and Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Teresa Wu
- ASU-Mayo Center for Innovative Imaging, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
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Adeoye J, Akinshipo A, Koohi-Moghadam M, Thomson P, Su YX. Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review. Front Oncol 2022; 12:976168. [DOI: 10.3389/fonc.2022.976168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
BackgroundThe impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.MethodsPubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ResultsML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.ConclusionOverall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designsSystematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.
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Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images. Cancers (Basel) 2022; 14:cancers14235897. [PMID: 36497378 PMCID: PMC9738124 DOI: 10.3390/cancers14235897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
In this work, we introduced an automated diagnostic system for Gleason system grading and grade groups (GG) classification using whole slide images (WSIs) of digitized prostate biopsy specimens (PBSs). Our system first classifies the Gleason pattern (GP) from PBSs and then identifies the Gleason score (GS) and GG. We developed a comprehensive DL-based approach to develop a grading pipeline system for the digitized PBSs and consider GP as a classification problem (not segmentation) compared to current research studies (deals with as a segmentation problem). A multilevel binary classification was implemented to enhance the segmentation accuracy for GP. Also, we created three levels of analysis (pyramidal levels) to extract different types of features. Each level has four shallow binary CNN to classify five GP labels. A majority fusion is applied for each pixel that has a total of 39 labeled images to create the final output for GP. The proposed framework is trained, validated, and tested on 3080 WSIs of PBS. The overall diagnostic accuracy for each CNN is evaluated using several metrics: precision (PR), recall (RE), and accuracy, which are documented by the confusion matrices.The results proved our system's potential for classifying all five GP and, thus, GG. The overall accuracy for the GG is evaluated using two metrics, PR and RE. The grade GG results are between 50% to 92% for RE and 50% to 92% for PR. Also, a comparison between our CNN architecture and the standard CNN (ResNet50) highlights our system's advantage. Finally, our deep-learning system achieved an agreement with the consensus grade groups.
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11
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Zhou X, Yue X, Xu Z, Denoeux T, Chen Y. PENet: Prior Evidence Deep Neural Network for Bladder Cancer Staging. Methods 2022; 207:20-28. [PMID: 36031139 DOI: 10.1016/j.ymeth.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 08/10/2022] [Accepted: 08/21/2022] [Indexed: 10/31/2022] Open
Abstract
Bladder cancer is a heterogeneous, complicated, and widespread illness with high rates of morbidity, death, and expense if not treated adequately. The accurate and exact stage of bladder cancer is fundamental for treatment choices and prognostic forecasts, as indicated by convincing evidence from randomized trials. The extraordinary capability of Deep Convolutional Neural Networks (DCNNs) to extract features is one of the primary advantages offered by these types of networks. DCNNs work well in numerous real clinical medical applications as it demands costly large-scale data annotation. However, a lack of background information hinders its effectiveness and interpretability. Clinicians identify the stage of a tumor by evaluating whether the tumor is muscle-invasive, as shown in images by the tumor's infiltration of the bladder wall. Incorporating this clinical knowledge in DCNN has the ability to enhance the performance of bladder cancer staging and bring the prediction into accordance with medical principles. Therefore, we introduce PENet, innovative prior evidence deep neural network, for classifying MR images of bladder cancer staging in line with clinical knowledge. To do this, first, the degree to which the tumor has penetrated into the bladder wall is measured to get prior distribution parameters of class probability called prior evidence. Second, we formulate the posterior distribution of class probability according to Bayesian Theorem. Last, we modify the loss function based on posterior distribution of class probability which parameters include both prior evidence and prediction evidence in the learning procedure. Our investigation reveals that the prediction error and the variance of PENet may be reduced by giving the network prior evidence that is consistent with the ground truth. Using MR image datasets, experiments show that PENet performs better than image-based DCNN algorithms for bladder cancer staging.
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Affiliation(s)
- Xiaoqian Zhou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Xiaodong Yue
- School of Computer Engineering and Science, Shanghai University, Shanghai, China; Artificial Intelligence Institute of Shanghai University, Shanghai, China.
| | - Zhikang Xu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Thierry Denoeux
- Sino-European School of Technology, Shanghai University, Shanghai, China; Université de technologie de Compiégne, Compiégne, France.
| | - Yufei Chen
- College of Electronics and Information Engineering, Tongji University, Shanghai, China.
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12
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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.
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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
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Hu X, Li G, Wu S. Advances in Diagnosis and Therapy for Bladder Cancer. Cancers (Basel) 2022; 14:cancers14133181. [PMID: 35804953 PMCID: PMC9265007 DOI: 10.3390/cancers14133181] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The clinical management of bladder cancer has been developing in the past decade, including diagnostic tools and treatment options. Both monotherapy and combination therapy have been undoubtedly upgraded. Multiple diagnostic techniques and therapeutic strategies have been developed to meet the urgent clinical needs, resulting in the emergence of various explorations for cancer diagnosis and therapy. In this review, we mainly focus on the advances in the diagnosis and treatment of bladder cancer. Abstract Bladder cancer (BCa) is one of the most common and expensive urinary system malignancies for its high recurrence and progression rate. In recent years, immense amounts of studies have been carried out to bring a more comprehensive cognition and numerous promising clinic approaches for BCa therapy. The development of innovative enhanced cystoscopy techniques (optical techniques, imaging systems) and tumor biomarkers-based non-invasive urine screening (DNA methylation-based urine test) would dramatically improve the accuracy of tumor detection, reducing the risk of recurrence and progression of BCa. Moreover, intravesical instillation and systemic therapeutic strategies (cocktail therapy, immunotherapy, vaccine therapy, targeted therapy) also provide plentiful measures to break the predicament of BCa. Several exploratory clinical studies, including novel surgical approaches, pharmaceutical compositions, and bladder preservation techniques, emerged continually, which are supposed to be promising candidates for BCa clinical treatment. Here, recent advances and prospects of diagnosis, intravesical or systemic treatment, and novel drug delivery systems for BCa therapy are reviewed in this paper.
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Affiliation(s)
- Xinzi Hu
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, Shenzhen 518000, China; (X.H.); (G.L.)
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Guangzhi Li
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, Shenzhen 518000, China; (X.H.); (G.L.)
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Song Wu
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, Shenzhen 518000, China; (X.H.); (G.L.)
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
- Correspondence:
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Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis. J Imaging 2022; 8:jimaging8060151. [PMID: 35735950 PMCID: PMC9225539 DOI: 10.3390/jimaging8060151] [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: 03/31/2022] [Revised: 05/06/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Segmentation of the bladder inner’s wall and outer boundaries on Magnetic Resonance Images (MRI) is a crucial step for the diagnosis and the characterization of the bladder state and function. This paper proposes an optimized system for the segmentation and the classification of the bladder wall. (2) Methods: For each image of our data set, the region of interest corresponding to the bladder wall was extracted using LevelSet contour-based segmentation. Several features were computed from the extracted wall on T2 MRI images. After an automatic selection of the sub-vector containing most discriminant features, two supervised learning algorithms were tested using a bio-inspired optimization algorithm. (3) Results: The proposed system based on the improved LevelSet algorithm proved its efficiency in bladder wall segmentation. Experiments also showed that Support Vector Machine (SVM) classifier, optimized by Gray Wolf Optimizer (GWO) and using Radial Basis Function (RBF) kernel outperforms the Random Forest classification algorithm with a set of selected features. (4) Conclusions: A computer-aided optimized system based on segmentation and characterization, of bladder wall on MRI images for classification purposes is proposed. It can significantly be helpful for radiologists as a part of spina bifida study.
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Towner RA, Smith N, Saunders D, Hurst RE. MRI as a Tool to Assess Interstitial Cystitis Associated Bladder and Brain Pathologies. Diagnostics (Basel) 2021; 11:diagnostics11122298. [PMID: 34943535 PMCID: PMC8700450 DOI: 10.3390/diagnostics11122298] [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: 10/13/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 11/29/2022] Open
Abstract
Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic, often incapacitating condition characterized by pain seeming to originate in the bladder in conjunction with lower urinary tract symptoms of frequency and urgency, and consists of a wide range of clinical phenotypes with diverse etiologies. There are currently no diagnostic tests for IC/BPS. Magnetic resonance imaging (MRI) is a relatively new tool to assess IC/BPS. There are several methodologies that can be applied to assess either bladder wall or brain-associated alterations in tissue morphology and/or pain. IC/BPS is commonly associated with bladder wall hyperpermeability (BWH), particularly in severe cases. Our group developed a contrast-enhanced magnetic resonance imaging (CE-MRI) approach to assess BWH in preclinical models for IC/BPS, as well as for a pilot study for IC/BPS patients. We have also used the CE-MRI approach to assess possible therapies to alleviate the BWH in preclinical models for IC/BPS, which will hopefully pave the way for future clinical trials. In addition, we have used molecular-targeted MRI (mt-MRI) to quantitatively assess BWH biomarkers. Biomarkers, such as claudin-2, may be important to assess and determine the severity of BWH, as well as to assess therapeutic efficacy. Others have also used other MRI approaches to assess the bladder wall structural alterations with diffusion-weighted imaging (DWI), by measuring changes in the apparent diffusion coefficient (ADC), diffusion tensor imaging (DTI), as well as using functional MRI (fMRI) to assess pain and morphological MRI or DWI to assess anatomical or structural changes in the brains of patients with IC/BPS. It would be beneficial if MRI-based diagnostic tests could be routinely used for these patients and possibly used to assess potential therapeutics.
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Affiliation(s)
- Rheal A. Towner
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma, OK 73104, USA; (N.S.); (D.S.)
- Correspondence: ; Tel.: +1-405-271-7383
| | - Nataliya Smith
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma, OK 73104, USA; (N.S.); (D.S.)
| | - Debra Saunders
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma, OK 73104, USA; (N.S.); (D.S.)
| | - Robert E. Hurst
- Department of Urology, University of Oklahoma Health Sciences Center, Oklahoma, OK 73104, USA;
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Hammouda K, Khalifa F, El-Melegy M, Ghazal M, Darwish HE, Abou El-Ghar M, El-Baz A. A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens. SENSORS (BASEL, SWITZERLAND) 2021; 21:6708. [PMID: 34695922 PMCID: PMC8538079 DOI: 10.3390/s21206708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/01/2021] [Accepted: 10/04/2021] [Indexed: 11/16/2022]
Abstract
Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs' edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (CNNS), 150 × 150 (CNNM), and 200 × 200 (CNNL), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The (CNNL) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70-0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems' results, we used the standard ResNet50 and VGG-16 to compare our CNN's patch-wise classification results. As well, we compared the GG's results with that of the previous work.
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Affiliation(s)
- Kamal Hammouda
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (K.H.); (F.K.)
| | - Fahmi Khalifa
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (K.H.); (F.K.)
| | - Moumen El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut 71515, Egypt;
| | - Mohamed Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Hanan E. Darwish
- Mathematics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt;
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Ayman El-Baz
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (K.H.); (F.K.)
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